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Every task used to start the same way for me, and you know this feeling:

You sit down to ship something small, and before you write a line, you ask, “Where is this documented?”

The context I needed was never in one place…

So I would dig through scattered READMEs, a stale runbook, an ADR1, and a lost Slack thread, then ask a team member. And when none of it worked, just guess.

None of it was the work itself, and over time, I realized it came down to three problems stacked on top of each other:

• First, the time. I burned hours hunting through docs and GitHub pages before I even started, just trying to find where this structure was explained.

• Then comes trust. So let’s say my AI agent finds something. How do I trust it when AI can hallucinate2?

• And the last one is the scariest: the knowledge never written down anywhere, the kind that lives in one senior engineer’s head and leaves the day they do.

These are real problems, and they cost real time and trust on every team.

The good news is you can solve all three with a single tool called NotebookLM.

So let me show you what it actually is…

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I want to introduce Gencay as the guest author.

He has been building with AI since ChatGPT first shipped in 2022.

For years, he built for clients on Upwork: 7,300+ hours of it.

Now he builds in the open and shares every piece of it on LearnAIWithMe, every Monday, Wednesday, and Friday.

Not just theory for its own sake. One real build at a time, from idea to running system.

Here’s what’s inside this newsletter:

What NotebookLM Actually Is

NotebookLM is a research tool from Google that answers your questions using a set of documents you provide.

The set you give it is called a notebook, just a workspace where you collect the files you want to read, like a folder you can ask questions to.

Picture a sharp new hire on their first day:

They are smart, and they want to help, but they do not know your system yet, so you hand them a stack of your real docs and tell them to work from those. This single constraint is the whole point.

They will not wing it from general knowledge, and when they tell you something, they can point to the page they got it from.

NotebookLM works differently from general chat models like ChatGPT/Claude. Those usually come from broad model knowledge.

Now you might ask, why not just bolt a RAG pipeline3 onto ChatGPT or Claude and be done with it?

Yes, you can, and sometimes it is the right call. But NotebookLM gives you grounding and citations out of the box with nothing to build or maintain.

(I’ll cover the real numbers on this later.)

Under the hood, the core mechanism behind NotebookLM is retrieval and grounding rather than open-ended model memory.

Here’s the workflow:

1. You ask a question in plain language.

2. NotebookLM searches your sources and pulls only the passages that match it.

3. Those passages, and nothing from the open web, go to Gemini4 as the context.

4. Gemini writes the answer from those passages.

5. Each claim comes back with a citation to the exact line it came from.

The technique has a name: retrieval-augmented generation (RAG).

It also leans on the notebook’s chat history while it answers, so a follow-up still makes sense without you repeating the setup.

Plus, it runs on Google’s Gemini models, which Google keeps upgrading, and it can hold a lot at once.

Each source can run up to about five hundred thousand words, and the chat works across a context window of a million tokens. So you can load real documentation instead of a handful of pages.

The one thing to keep in mind is scope.

Each notebook only knows what is inside it. It does not pull from your other notebooks. The boundary sounds like a limitation until you realize it is exactly what makes NotebookLM safe to put underneath an agent.

Enough about what it is… Let’s set one up.

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How to set up NotebookLM?

1. Visit here,

2. Click on “Try NotebookLM”,

3. Then click “Create new notebook”.

You have a few ways to feed it.

A web search, file upload, websites, a Google Drive connection, or pasted text.

Pick whichever matches where your material already lives.

For this notebook, I used websites.

I clicked “Websites” and pasted the Backstage links I had pulled together. The architecture decision records, the core docs, and the pull requests.

Here are the exact links I added:

https://backstage.io/docs/architecture-decisions/adrs-adr001/

https://backstage.io/docs/architecture-decisions/adrs-adr002/

https://backstage.io/docs/architecture-decisions/adrs-adr003/

https://backstage.io/docs/architecture-decisions/adrs-adr004/

https://backstage.io/docs/architecture-decisions/adrs-adr005/

https://backstage.io/docs/architecture-decisions/adrs-adr006/

https://backstage.io/docs/architecture-decisions/adrs-adr007/

https://backstage.io/docs/architecture-decisions/adrs-adr008/

https://backstage.io/docs/architecture-decisions/adrs-adr009/

https://backstage.io/docs/architecture-decisions/

https://backstage.io/docs/overview/architecture-overview/

https://backstage.io/docs/backend-system/architecture/index/

https://backstage.io/docs/features/techdocs/architecture/

https://backstage.io/docs/deployment/

https://backstage.io/docs/getting-started/

https://backstage.io/docs/overview/what-is-backstage/

https://github.com/backstage/backstage/pulls

https://github.com/backstage/backstage/tree/master/docs/architecture-decisions

A couple of minutes later, the notebook gets trained and is ready to answer.

From here, the notebook is ready, with every Studio output on the right waiting to build from your sources.

So the notebook is loaded.

But why trust a word it says? This comes down to one idea…

Grounding, in plain language

Here is what grounding looks like in practice:

I asked the notebook about the latest AI model Anthropic released, something nowhere in my sources, and here is its response:

To make the boundary clear, let’s customize the tone, using this prompt:

If the answer is not explicitly supported by the provided sources, respond only with:

Not in source.

I asked the same question after it, and here is the answer:

So I asked the same question again, and this time it just said: “Not in source.” One line in the prompt forced it to stay inside what I had uploaded instead of reaching for what it already knew.

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How citations work

If grounding5 is the promise, then citations are the receipt to prove it.

NotebookLM does hallucinate less, and there is a study to point at now:

Researchers ran ChatGPT, Gemini, and NotebookLM over the same 300-document reporting task. NotebookLM made something up in roughly 13% of its answers, against about 40% for the other two on the same sources.

Here is the research report.

Let me show you the notebook we will use for the rest of this newsletter.

It holds Backstage’s Architecture Decision Records. Backstage is Spotify’s open-source developer portal, and its decision log is public. So it makes a clean stand-in for a real team’s knowledge base.

Here is the prompt:

Summarize the key architecture decisions recorded in these sources.
For each decision, cite the specific Architecture Decision Record it comes from.

Here is the answer it returned, and the part worth watching is the citations:

Look at the numbers in the answer…

Each one is a citation. Click one, and NotebookLM opens the source and highlights the line the claim came from.

That is the difference between an answer you hope is right and an answer you can check in two seconds.

So that is what it does well. Just as important is what it does not.

What NotebookLM is not?

A PDF reader will happily open your PDF, but it just shows you the page and stops there.

NotebookLM reads across every source at once and tells you exactly where it looked, so it does much more than a plain reader would.

Also, it is not a full agent running your code. It reads your sources, grounds its answers in them, and cites them, but the actual building still happens in your own tools.

On its own, NotebookLM is a read layer, and a very good one at that. So keep this frame in mind, and you will reach for it at the right time.

You have seen what it is and what it is not, so the next question is how you actually set one up…

Engineering Use Case: NotebookLM as a Knowledge Layer for Code

This is the part I actually run…

The rest of the newsletter explains NotebookLM. This section puts it to work.

The setup is one notebook, loaded with a real engineering knowledge base, and a Claude Code agent querying it.

I am using the Backstage notebook from earlier, since its decision records, docs, and merged pull requests are all public. I loaded them into a single notebook and pointed my agent at it.

How I connected NotebookLM and Claude Code in the first place

We connect NotebookLM and Claude Code through a command-line interface. (It is not official.)

Here is the link: https://github.com/teng-lin/notebooklm-py

You can follow the setup steps on GitHub, or you can just drop this prompt into Claude Code:

Install the notebooklm-py command-line interface from
https://github.com/teng-lin/notebooklm-py
and ping me when I need to authenticate.

It opens the browser. You authenticate once.

From then on, the agent can reach your notebooks.

Now, let me show you how it reaches the Backstage notebook…

Use this prompt:

Using the notebooklm-py command-line interface, connect to my Backstage notebook
and ask it to summarize ADR003 through ADR007
so I can see the decisions at a glance.
Return the answer with the citation for each ADR.

Backstage notebook link: (paste link here)

First, it authenticates, then it summarizes ADR003 through ADR007.

Here it is:

Imagine if accessing the right document while building was this easy. How much time would you save?

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How agent queries on every Pull Request

The goal here is simple:

Just catch a change breaking a documented decision before a human spends time reviewing it.

So, on every pull request, the agent reads the diff and then asks the notebook one question via the command-line interface. “Does this change follow our recorded decisions?”

The notebook answers from the architecture decision records, with a citation, and the agent flags or passes on the answer.

Here is the prompt the agent runs:

Using the notebooklm-py command-line interface, query my Backstage notebook.

A pull request adds a new catalog entity kind called “Datastore”.

Its YAML descriptor has no api Version field and no metadata.name.

Based on the architecture decision records and the default catalog file format,
does this change follow our documented conventions?

Answer only from the sources and cite the specific record.

Backstage notebook link: (paste link here)

Now the agent has something concrete to check against, so here is what it flagged:

Look at what came back.

The agent did not just say “this looks wrong.” Instead, it flagged three separate problems and tied each one to a specific record.

It holds no opinion about your architecture. So it asked the notebook, and the notebook answered from Backstage’s real records.

That is what turns architecture review from tribal knowledge stuck in one person’s head into a check that the agent can run on every change.

A concrete example, answered with citations.

The PR check above is the whole idea in one shot. A change comes in, the agent asks the notebook, the notebook answers with the exact records the change breaks.

Take the strongest of the three flags, the Datastore kind. To see what the agent is checking against, ask the record directly:

Using the notebooklm-py command-line interface, query my Backstage notebook.

What does ADR005 decide about catalog core entities,
and would a new “Datastore” entity kind fit those decisions?

Quote the relevant lines and cite the source.

Backstage notebook link: (paste link here)

Here is what the record gave back:

Look at what the record itself says. The agent pulled ADR005 straight from the source, hash and all, and quoted it verbatim.

The catalog stands on three core entities: Components, APIs, and Resources. A datastore is not a fourth kind. It is already a Resource, the same bucket Backstage uses for Bigtable databases and S3. So the proposed change was never going to fit, and the record said so in its own words.

Now the design-doc check. The same question works before a review meeting, when the change is still a proposal:

Using the notebooklm-py command-line interface,
query my Backstage notebook:(paste the link here)

Here is a new design doc proposing a dedicated “Datastore” entity kind for databases.

Does this conflict with any existing architecture decision?

Cite the record it conflicts with.

Here is what came back this time.

Same answer, different door. The first query asked what ADR005 decides.

This one asks whether a new design conflicts with it:

Both land on the same record, the same source hash, the same verbatim lines.

One reads it as a PR check, the other as a design review before the meeting. The notebook does not change its story between the two, because it is reading, not guessing.

Detailed workflows

The same loop ends up covering four different jobs, so it is one notebook and one query pattern paying off in four places:

• PR review. The agent checks the diff against the records and tells you whether the change follows the decisions you already made. Say a new teammate opens a PR that quietly reintroduces a pattern the team dropped a year ago. A human reviewer might not remember the old decision, but the notebook does. So the agent flags it with the exact record, before anyone burns a review cycle on it. This is the Datastore case from earlier, running on autopilot.

• Architecture and design review. The agent runs the design doc against the decision log before the meeting, so a conflict surfaces while the design is still a proposal. You drop in a proposal for a new caching layer. The agent points out that it contradicts a trade-off the team settled on two quarters ago, and it provides the citation. The meeting then starts from there instead of relitigating it live, which is usually where the hour goes.

• New feature spec. The agent pulls the records touching the area you are working in, so the spec starts from what the team has already decided. Before you write a line of the spec for a new auth flow, the agent surfaces the history for you. Every past decision, constraint, and rejected approach in this corner of the system. You stop proposing things the team already ruled out, and the reviewers stop typing the same objections they typed last time.

• Debugging against past incidents. The agent queries old PRs and notes for the same symptom to determine whether the team has encountered this failure before. A service starts timing out under load, so you describe the symptom. The agent finds the same failure from eight months ago, along with the fix that actually worked. Instead of rediscovering it from scratch at 2 am, you are reading the answer someone already paid for once.

Every one of them is really the same move: you ask the notebook, you get a cited answer, and you act on it.

The code layer is where I live, but the same notebook quietly does more.

Let me show you…

Alternative Use Cases for Engineers

Once you wrap it in a Claude routine6, most of it runs without you.

Let me show one, then point at the rest…

The one I run on a schedule: weekly PR reporting

A Claude routine queries the notebook on a schedule.

Every Friday, it checks the week’s merged PRs against our recorded decisions and sends a short report. Which changes followed the records, which drifted, and the citation for each call.

Here is the prompt the routine runs:

Build a routine that runs every Friday at 5pm.

Use the notebooklm-py command-line interface to query my Backstage notebook:
(paste the link here)

Review the merged pull requests in the sources against the architecture decision records.

For each PR, say whether it follows our recorded decisions or drifts from them,
and cite the specific ADR.

Send me a report with one line per PR
and a single summary line for the team lead at the end.

Next, Claude Code built the routine.

Now let’s head to the routine inside Claude Code.

Click on the one you just set up.

Click on “Run now” to test it.

Here is what the first run produced.

The first run came back empty.

The notebook had no merged PRs inside it, only open ones, so there was nothing to check against the records.

It did not invent a result. Instead, it told me the sources held no merged PRs and stopped.

This empty result is reassuring because it shows the workflow tells you when there is nothing to report, rather than inventing something to fill the gap.

So I added a real merged PR as a source and ran it again.

Here is the result:

From here, you can have Claude send this report wherever you want. Slack, email, or any tool you already use.

You have seen what it does. Now here is where it wins and where it does not…

Let’s keep going!

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A general-purpose AI model, even with access to clinical notes, still does not write like a clinician…

Give the model access to legal documents, and it still does not draft like a lawyer…

The vocabulary is familiar, but the format and conventions are NOT present by default.

Ask a foundation model to write up a patient visit, and you get:

“The patient is a 70-year-old man with a history of hypertension
and hyperlipidemia who presents to the emergency department
complaining of substernal chest pain, pressure-like,
radiating to the left arm and jaw, with nausea, diaphoresis,
and shortness of breath.”

While a clinician writes:

“70 y/o M w/ h/o HTN, HLD presents to ED c/o substernal CP.
Pressure-like, radiates to L arm and jaw. + nausea, diaphoresis, SOB.”

Same content…

One is a paragraph; the other is a chart entry. The general model knows the medicine; it doesn’t know how clinicians write it down.

Fine-tuning is how you get them there.

Rather than prompting the model into a style on every call, you train those patterns directly into the model’s weights so they become its defaults. Fine-tuning is how tools like Harvey write like junior associates at large law firms.

• Glass Health produces clinical notes in the format clinicians use.

• BloombergGPT reasons over financial data the way analysts expect.

• GitHub Copilot responds fast enough to feel like autocomplete.

Each of these is a general-purpose model trained in a narrow domain.

This newsletter covers how fine-tuning works, when it is the right choice over prompting or RAG, and how to build it.

Let’s dive in!

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AI is in your engineering workflow. While the token spend shows it, the throughput doesn’t. The human is very much still in the loop, and that’s a context problem.

Join live July 23 (FREE) to learn:

• The 8 levels of context maturity: where most teams are stuck and what the ceiling looks like at each stage

• Why more MCPs, rules, and skills provide agents access but not understanding

• How leading engineering teams are using a context engine to make the most of their agents

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I want to reintroduce Louis-François Bouchard as the author of this newsletter.

He’s a best-selling author (Building LLMs for Production), the co-founder of Towards AI, and the creator of the YouTube Channel, What’s AI, where he helps people understand AI and learn how to apply it in the real world.

Through his development work with clients and his content, teaching, and AI training programs on the Towards AI Academy, Louis focuses on making AI practical for builders, engineers, and curious learners alike.

At Towards AI, he and his team train AI engineers through courses built for every stage, from beginner to advanced. That educational mission and the real-world experience building for his clients are exactly why I wanted him in this newsletter series.

Here’s what’s inside this newsletter:

• What fine-tuning change in a model? Behavior, style, and domain reasoning patterns.

• Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA). The methods that make fine-tuning practical by modifying less than 1% of a model’s weights.

• Data quality. Instruction-response pairs, dataset size, synthetic data, and the gap between training distribution and production.

• The decision framework. When prompting is enough, when RAG is the right addition, when fine-tuning is warranted, and when all three are needed together.

• Alignment beyond Supervised Fine-Tuning (SFT). Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), model merging, and distillation.

• A hands-on comparison. Three configurations for the same clinical SOAP note task: base model, fine-tuned model, and frontier model ceiling.

Golden members get all letters like these!…

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Why Build Your Own Domain Expert AI?

Models such as GPT-5.5, Claude Opus 4.7, and Gemini 2.5 Pro are foundation models: Large Language Models (LLMs) trained on broad, internet-scale text to perform many tasks reasonably well rather than one task very well.

Their defaults are calibrated for a wide audience, not a specific domain.

For a domain product, this means out-of-the-box quality is just the starting point, and specialist users typically expect more…

• Domain vocabulary and reasoning. A foundation model recognizes terms such as “Transient Ischemic Attack” and “force majeure,” but recognition is not the same as application. It does not consistently apply the National Institutes of Health (NIH) Stroke Scale, the standard scoring system clinicians use to grade stroke severity. It does not draft a force majeure clause the way a specific law firm drafts it. Fine-tuning bakes those specialized patterns into the model’s weights so they appear by default, without requiring them to be prompted on every call.

• Tone and style consistency. Every domain has a house style. Brand voice for marketing, a formal style for legal memos, a structured format for clinical chart notes. Harvey reads like a junior associate at a large law firm because the underlying model has been customized for legal drafting work. Prompting can imitate a style for a turn or two, but the model usually drifts back to its default voice within a few turns.

• Latency and model size. A fine-tuned 7-billion-parameter (7B) model often beats a foundation model on a narrow task. On a single Graphics Processing Unit (GPU), a fine-tuned 7B model can produce its first output token in under 300 milliseconds. This metric is called Time-To-First-Token (TTFT). For a product like code autocomplete, where suggestions must appear before the user types the next keystroke, this latency is the difference between a usable and an unusable product. GitHub Copilot is fine-tuned for exactly this reason.

• Cost at scale. Above a few hundred thousand queries per month (lower against a flagship-plus-RAG configuration, higher against a mini-class API), a self-hosted fine-tuned model is cheaper than paying for foundation-model Application Programming Interface (API) calls. Below this volume, the work required to build the dataset and run training usually costs more than a year’s worth of foundation-model API bills.

• Data privacy. Some training data cannot leave your infrastructure. Patient charts are regulated under the Health Insurance Portability and Accountability Act (HIPAA), the attorney-client privilege protects legal documents, and customer records may be regulated under the General Data Protection Regulation (GDPR) or System and Organization Controls 2 (SOC 2). None of this data can be sent to a third-party API for processing. Open-weight fine-tuning addresses this constraint by keeping the model, training data, and inference traffic within your own Virtual Private Cloud (VPC), behind your own authentication, and with your own audit trail.

Before deciding to fine-tune, you must confirm prompting and Retrieval-Augmented Generation (RAG) do NOT meet your quality bar.

Most people overestimate how much fine-tuning will buy them over a well-engineered prompt combined with retrieval. Fine-tuning is the right answer when one of the five reasons above applies. But it is the wrong answer when you are reaching for it because the prompting felt inconvenient.

Once you have decided to fine-tune, three design questions determine the system: which base model to start with, which dataset to build on, and whether to pair fine-tuning with retrieval.

What We Are Building

The system has four components:

• Base model is the pretrained Large Language Model (LLM) the fine-tuning pipeline starts from.

• Dataset is a set of instruction-response pairs capturing the behavior the model should produce by default.

• Fine-tuning pipeline uses that dataset to shift the base model’s behavior toward the target domain.

• API endpoint serves the resulting model, paired with the retrieval layer from newsletter 3, for use cases where the domain also requires fresh facts.

Requirements

• System must produce outputs in the domain style and format without per-query examples, and apply domain reasoning without Chain-of-Thought prompting on every call.

• It must refuse out-of-scope questions in a calibrated way rather than attempting an answer.

• The entire training run must be reproducible, with a locked dataset, locked random seeds, and a versioned model card.

• A production ship would also verify that general capability regresses by less than 5% on Massive Multi-task Language Understanding, Professional version (MMLU-Pro) or MT-Bench, and that inference cost is at least 50% below the foundation-model baseline. Both gates are described in Parts 3 and 4 but are not run in Part 5’s walkthrough.

• One advanced feature covered later: preference tuning with Direct Preference Optimization (DPO), used to correct behaviors that Supervised Fine-Tuning (SFT) cannot reach reliably.

What this system does not do

It does not call tools or take actions, which is the focus of a future newsletter.

Also, it does not guarantee factual freshness, because the model’s training data is fixed at the point of fine-tuning. For current facts, RAG is still the right layer to add.

Deployment

Part 5 builds the open-weight path and documents the OpenAI fine-tuning path as a hosted alternative.

The open-weight path uses a base model such as Llama, Qwen, or Mistral with LoRA adapters served on vLLM1. The hosted path uses the OpenAI fine-tuning API.

Model selection

The right starting point is the smallest base model that already understands the domain.

Here are three criteria to look for:

1. Run a 20-question domain probe on the base model; if it cannot answer roughly half correctly, fine-tuning will not recover it, because fine-tuning shifts defaults and does not install knowledge the model never had.

2. Confirm fine-tuning support: on the open side, Llama, Qwen, and Mistral all support it; on the hosted side, OpenAI API currently exposes GPT-4.1 and GPT-4.1-mini for fine-tuning, since the GPT-5.x reasoning tier is not available for SFT at the time of writing.

3. Check license fit: Llama community license, Apache 2.0, and the OpenAI Terms of Service each impose different constraints on where the model can be hosted, which determines whether the model can run where your data lives.

Start in the 7B-8B parameter range and scale up only if the domain probe requires it.

Model choice is the last decision you make before training begins.

The next part covers what training actually does to the model you picked…

Part 1: How Fine-Tuning Changes a Model

Pretraining runs next-token prediction over trillions of tokens of text scraped from the internet, books, and code repositories.

By the end of the process, the model has compressed a broad representation of human language and knowledge into its weights. This is the base model: capable across many tasks, specialized in none.

Fine-tuning starts from that base and runs the same training process again, but on a much smaller dataset, typically a few thousand examples, chosen specifically to push the model’s behavior in one direction. The model is not learning language at this stage. Instead, it is learning which of its existing patterns to apply by default.

i.e., fine-tune for behavior, retrieve for knowledge.

Fine-tuning works by reinforcing patterns across many examples until they override the base model’s defaults. A reasoning pattern or output format repeated across thousands of training examples will stick.

A fact that appears once or twice will not, because the model has no reason to weigh it more than the vast body of knowledge it acquired during pretraining.

What Gets Updated During Training

Fine-tuning adjusts every parameter in the model slightly, one example at a time, until its behavior shifts toward what the dataset shows.

The primary cost here is memory:

A 7B model has 7 billion numbers stored on the GPU. Training requires storing a second number alongside each parameter to track how it should change, roughly doubling memory usage to 28 GB.

The optimizer adds a few more bookkeeping values per parameter on top of that, pushing the total GPU memory to 60-80 GB.

Consumer GPUs have 16 to 24 GB of memory, which is why a full fine-tune of a 7B model used to require a cluster and cost tens of thousands of dollars. Parameter-efficient methods (covered later) reduce the requirement to a single 16 GB GPU and bring the cost down to around $10 per run.

The size of the GPU bill forces the choice of training regime; the next decision a team has to make…

Three Training Regimes

There are three ways to fine-tune a model, each using a different kind of dataset:

• Continued pretraining runs the same kind of training the base model already underwent, but on raw text from a single domain rather than the open internet. There are no labels; the model just reads a lot of domain text and gets better at predicting the next word in the domain. The dataset is large, ranging from hundreds of millions to billions of words, and the point of the exercise is to teach the model the field’s vocabulary. BloombergGPT started this way with financial text, and Med-PaLM (a medical fine-tuning of Google’s PaLM, Pathways Language Model) started this way with medical text. You should reach for continued pretraining only when the base model barely understands your domain.

• Supervised Fine-Tuning (SFT), introduced in newsletter 1, trains on a list of questions paired with the answers the model must produce. The model learns to imitate those answers. Typically, the datasets contain 1,000 to 50,000 of these question-answer pairs. SFT is the right starting point for almost every fine-tuning project, because most domain problems are about style, format, and reasoning patterns rather than missing knowledge.

• Preference tuning, which includes both Reinforcement Learning from Human Feedback (RLHF) and DPO, trains on comparisons. For each question, the dataset contains two possible answers and a label indicating which is better. The model learns to produce answers closer to the preferred one. Datasets are smaller, typically 500 to 5,000 pairs. Preference tuning is used to correct behaviors that SFT cannot reliably teach: calibrated refusals, a consistent tone, and output formats that hold up even when users push back.

The practical order is to start with SFT, ship it, identify the specific behaviors that still need work, and then add DPO to address those cases.

Continued pretraining enters the picture “only” if SFT stops improving and the base model clearly does not understand the domain vocabulary.

SFT handles the bulk of the work in a typical project, and the question it raises immediately is how to run it without an 80 GB GPU. The answer is a class of methods called Parameter-Efficient Fine-Tuning (PEFT), which trains only a small fraction of the model’s parameters rather than all of them.

LoRA is the most widely used method in this class…

Low-Rank Adaptation (LoRA)

LoRA leaves the base model’s weights completely frozen and instead trains a small side network alongside it.

The side network is built from two small matrices, conventionally labeled A and B. At inference time, the output of the side network is added on top of the frozen base model’s output.

Only the side network is trained; the base model never changes.

This works because the change required to turn a general model into a domain expert is mathematically small.

You do not need to adjust every parameter in the model; you only need to apply a targeted correction to the parameters that matter. The side network learns the correction. Its capacity is controlled by a setting called the rank, typically 8 or 16, which determines how much the side network can express.

In those settings, LoRA trains fewer than 1% of the parameters full fine-tuning would touch and recovers 90-95% of the quality on most tasks. GPU memory drops by an order of magnitude because the optimizer only tracks the small side network, and the risk of degrading the base model remains low because its weights never change.

Yet LoRA has two trade-offs against full fine-tuning:

• First, on most tasks, it matches full fine-tuning in quality, but not always at parity. On hard tasks, full fine-tuning is, on average, a few percentage points stronger.

• Second, the side network has a fixed capacity ceiling set by the rank: a rank-16 adapter can only encode so much change to the base model. For large domain shifts, such as moving a general model toward dense medical or legal reasoning, LoRA hits this ceiling and stops improving with more data.

Full fine-tuning has no such ceiling, but it incurs the highest GPU cost and carries the greatest risk of the model forgetting its general capabilities.

It is worth considering only when LoRA has plateaued, and the GPU budget allows it.

Reminder: this is a teaser of the subscriber-only newsletter series, exclusive to my golden members.

When you upgrade, you’ll get:

• Simple breakdown of real-world architectures

• Frameworks you can plug into your work or business

• Proven systems behind ChatGPT, Perplexity, and Copilot

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Quantized Low-Rank Adaptation (QLoRA)

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In the early days, running apps was simple.

Most applications ran on a single server, and managing them was quite easy. Then microservices became popular. Instead of a single app on a single server, companies were running hundreds of apps across hundreds of servers.

But managing this became extremely difficult.

Engineers had to decide where apps should run, what to do when apps crash, how to handle more traffic, and how to update apps without downtime. Most people handled this with scripts and manual work. It worked for a while, but as systems grew, system started breaking more often, and downtime became more common.

Google faced this problem on a massive scale.

So they built an internal system called Borg. Borg was used to manage apps across thousands of servers. It automates scheduling, scaling, and self-healing across many servers. Years later, Kubernetes (k8s) was built based on many of the same ideas as Borg and got released as an open-source system. Since then, it has become the standard platform for running distributed systems and microservices.

Kubernetes could look simple from the outside, but within the cluster, many components constantly work to keep your apps running.

Let’s look at what is happening behind the scenes…

The most dangerous app in your company was built by someone in finance last Tuesday (Partner)

The number of vibe-coded apps in enterprise environments has skyrocketed.

Yet most vibe-coded apps cannot handle customer data securely, comply with your internal workflows, or pass production processes.

i.e., they “break” your company standards.

So your apps become an IT problem when you ask yourself these questions:

• Where does the data live & who can access the app?

• Where are the audit logs & how to enforce company security policies?

• Can this safely reach production?

INTRODUCING SUPERBLOCKS…

(Instead of cleaning up later, it makes IT the foundation from day one.)

• Apps deploy inside your own AWS account and VPC, so sensitive data stays under your control.

• Company login, role-based permissions, audit logs, and governance get enforced automatically across all apps.

• Every deployment gets scanned for vulnerabilities before it reaches production.

So you build production-ready apps instead of demos that need to be rebuilt later…and your business moves faster.

If you’re serious about enterprise AI app development, try Superblocks right now:

TRY SUPERBLOCKS NOW

(Thanks to Superblocks for partnering on this post.)

I want to introduce Ayaan as a guest author.

He’s a Certified Kubernetes Administrator (CKA) who writes about Kubernetes, cloud infrastructure, and cloud-native systems. He builds in public and shares practical tips, projects, and lessons learned along the way.

Check out his work & socials:

• Twitter

• Github

Here’s what you’ll find inside this newsletter:

• The mental model that makes Kubernetes click. The reconciliation loop, what it is, why it matters, and why most Kubernetes confusion comes from not understanding it early enough.

• A complete breakdown of the Kubernetes architecture. Every component in the control plane and worker nodes, what each one does, how they talk to each other, and what breaks when any one of them goes wrong.

• From kubectl apply to a running pod, step by step. The exact sequence of events inside the cluster from the moment you hit enter to the moment your app is live.

• Every core Kubernetes object explained. Pods, deployments, daemonsets, statefulsets, services, ingress, secrets, what each one does, when to use it, and when people reach for the wrong one.

• What Kubernetes genuinely gets right and where it gets complicated. An honest look at the trade-offs most explainers skip over, including etcd as a single point of failure, the complexity floor, and the hidden cost nobody talks about.

• When NOT to use Kubernetes. The answer most posts won’t give you.

Most Kubernetes posts teach you how to use it…

This newsletter is about how it actually works, because that’s the part that makes everything else make sense.

Kubernetes Architecture Overview

To understand Kubernetes, we first need to look at its architecture.

Then we can dive into the individual components…

Kubernetes architecture is a distributed system:

It doesn’t run on just one server. Instead, it operates on a group of servers that work together, known as a Kubernetes Cluster. These servers could be virtual machines or bare metal servers. The components of Kubernetes are distributed across these servers over a single network.

At the highest level, the Kubernetes cluster consists of two key components: control plane and worker nodes.

We will go through each of these components in detail later...

Control Plane Node

Think of the control plane as the brain of the Kubernetes cluster.

It manages global decisions for the cluster. Plus, it schedules apps, checks system health, and ensures the cluster remains in the desired state.

The control plane contains these components:

• kube-apiserver

• etcd

• kube-scheduler

• kube-controller-manager

• Cloud-controller-manager

Worker Node

Imagine the worker node as the muscle of the Kubernetes cluster.

It’s responsible for running workloads (containerized applications) in the cluster. And it contains these components:

• kubelet

• kube-proxy

• Container runtime

i.e., control plane decides what runs where; worker nodes run the containers.

Kubernetes continuously checks and fixes the system. This ensures everything runs as expected…

A cluster can have many control plane nodes and worker nodes; this usually depends on the number of workloads to be deployed.

In production systems, it’s typical to have many control plane and worker nodes. This setup keeps the system stable. It also ensures applications run smoothly, even if some machines fail or are unavailable.

Plus, Kubernetes has a strong, decentralized structure. It automatically moves workloads to healthy nodes upon node failure. This keeps your system running smoothly.

This makes Kubernetes the foundation of a reliable and self-healing distributed system.

Desired State and Actual State

One of the most important ideas in Kubernetes architecture is the concept of desired state and actual state.

When we deploy an application to Kubernetes, we don’t start it in the cluster ourselves. Instead, we tell Kubernetes what we want the system to look like; for example, we could say we want three replicas of an application running.

This description of what we want is called the desired state.

The actual state is what is currently running in the cluster.

Kubernetes always checks the desired state against the actual state. If it finds a difference, it automatically tries to fix it.

For example, if one app crashes or a node fails, the actual state no longer matches the desired state. Kubernetes detects this and automatically creates a new app on healthy nodes to bring the system back to the desired state. The ongoing task of checking and fixing the system is called the reconciliation loop.

This is a key concept in how Kubernetes operates.

Now we understand Kubernetes architecture, let’s learn the control plane and worker nodes…

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Kubernetes Control Plane Core Components

We already saw how the control plane makes global decisions for the cluster, but it does NOT do that as a single component.

Instead, control plane is divided into five components that work together to manage the entire cluster state.

These components have their own specific roles:

• One component is the central hub for all cluster communication.

• Another stores the cluster data.

• One makes scheduling decisions.

• Another continuously monitors cluster health and keeps it in the desired state.

• And the last one integrates Kubernetes with your cloud provider to manage resources such as load balancers, routes, and storage volumes.

Let’s look at each of the control plane components and the concepts behind each:

1. Kube-Apiserver

The kube-apiserver is central to the Kubernetes control plane.

It serves as the key hub of the cluster, exposing the Kubernetes API. Almost every operation in the Kubernetes cluster goes through the API server. The other cluster components don’t talk directly with each other. Instead, they use the kube-apiserver to read and update the cluster state. This lets the parts work independently but still connect through a central system. Also, when you interact with Kubernetes, you do not interact with the Kubernetes nodes and their components directly.

Instead, you interact with it via kube-apiserver.

It handles your requests and updates the cluster state.

All messages between the API server and other components are encrypted using TLS1. This keeps unauthorized users out and protects communication within the cluster.

i.e., API server is central communication hub of Kubernetes; without it, the cluster cannot function.

Key Responsibilities of Kube-Apiserver

1. Exposes cluster API endpoint: It’s the only component in the control plane that the outside world can talk to. When you run a kubectl command (kubectl is the command-line tool to interact with Kubernetes), it sends a request to the API server over HTTPS. When your app gets deployed in the cluster, it’s talking to the API server.

2. Single entry point for all operations: All requests to the cluster go through the API server. This includes requests from you and from internal Kubernetes components. Components do NOT communicate directly with each other. They talk only to the API server. Because of this, API server always has a complete picture of what is happening in the cluster.

3. Authentication and Authorization: It checks who is making the request. Then, it confirms if they have permission to take the action. If either check fails, the request stops right there.

4. Notifies the rest of the system when something changes: Once a change is saved, API server notifies every interested component.

5. Stores cluster state in etcd: Kubernetes keeps all cluster details in etcd. This is a fast, lightweight database made for storing cluster state. Nothing reads or writes to it directly except the API server. Every change has to go through it first, this keeps the data clean and consistent.

Since the API server stores all cluster state in etcd, etcd becomes the source of truth for the entire Kubernetes cluster.

Let’s now look at etcd and understand its role in the control plane…

2. Etcd

etcd is a distributed key-value store (distributed database) that Kubernetes uses to store the entire cluster data.

It keeps track of what is running, what should run, and how each Kubernetes component is set up. Since it stores all data about the cluster, it is also called the single source of truth for the Kubernetes cluster.

Kubernetes API server stores all information ever received in etcd.

The scheduler and controller manager monitor the API server for changes. They help keep the cluster in its desired state. The API server reads data from etcd. This makes etcd the central storage system for the entire cluster.

etcd is a key part of the Kubernetes control plane.

It’s crucial to back it up regularly in production settings. If etcd data is lost, the cluster loses its state. So Kubernetes won’t know what applications are running, what nodes exist, or what the system’s desired state is.

i.e., etcd is the database that stores everything about the cluster.

Key Responsibilities of Etcd

• Stores cluster state: All Kubernetes objects are stored in etcd. This includes Pods, Deployments, Services, Nodes, ConfigMaps, and Secrets.

• Acts as the source of truth: The entire cluster state is stored in etcd, and all the components in the cluster rely on this data to make decisions.

• Enables cluster recovery: Backups of etcd can be used to restore the entire cluster state in case of failure.

Also, etcd is the only StatefulSet component in the control plane.

i.e., etcd must be carefully managed and backed up, as it contains the entire cluster state.

Now we know how Kubernetes stores its cluster state; let’s explore how it decides where to run applications…

3. Kube-Scheduler

The kube-scheduler decides which node will run a pod.

A pod is the smallest unit you can deploy in Kubernetes. It acts as a wrapper for one or more containers. Whenever a pod is created, it remains in the cluster without a node being assigned to it.

The scheduler always checks for these pods.

It assigns them to the right node based on available resources and pod setup.

Let’s learn how the scheduler works:

1. Watches for unassigned Pods: The kube-scheduler monitors the API server. It checks for pods that haven’t been assigned to any node after creation.

2. Gets the list of available nodes: It gets the information about all worker nodes and their available resources.

3. Filters Nodes: The scheduler removes nodes that don’t meet the pod's requirements. This includes:

   • Not enough CPU or memory

   • Not enough GPU

   • Node is under maintenance

   • Pod needs a GPU, but the node lacks one

4. Scores for remaining nodes: After removing some nodes, it scores each remaining node. It considers resource availability, scheduling policies, load balancing, and so on. Then, it selects the node with the highest score from the filtered list.

5. Updates the Pod information: The scheduler updates the Pod object via the API server and assigns the selected node.

i.e., scheduler does not run the pod itself; it only decides which node the pod should run on.

Once the scheduler assigns a Pod to a node, kubelet on that node is responsible for actually running the container.

4. Kube-Controller-Manager

The kube-controller-manager is part of the control plane.

It monitors the cluster and ensures the actual state matches the desired state. It runs multiple controllers, each handling a specific task in the cluster. These controllers monitor the cluster’s state using the Kubernetes API.

If they find a mismatch between the desired and actual state, they work to correct it.

To understand this better, let’s look at an example:

Imagine an application on Kubernetes set to run three replicas. This is the cluster’s desired state. But one of the replicas crashes, and the actual state becomes 2 instead of 3. The controller will detect this difference and will automatically create a new replica of the application to make it three again. This is how the controller continuously monitors the cluster state and matches it with the desired state.

Kubernetes is self-healing and highly automated because it continuously monitors the cluster. It resolves differences between the desired and actual states.

Here is a list of important built-in Kubernetes controllers:

• Node Controller: Detects node failures and responds when nodes become unavailable.

• Replication Controller: Ensures the correct number of Pod replicas are running.

• Deployment Controller: Handles rolling updates and ensures Deployments maintain the desired state.

• Endpoint Controller: Updates endpoints for Services when Pods change.

• Namespace Controller: Manages namespace lifecycle and resource cleanup.

• Service Account Controller: Creates and manages service accounts and tokens.

• Job Controller: Ensures jobs are completed successfully.

All the controllers in Kubernetes work as a control loop.

i.e., it continuously monitors the cluster state via the API server and compares the actual state with the desired state. Kubernetes has a control loop feature that’s key to its design.

This allows for self-healing, auto-scaling, and rolling updates.

5. Cloud Controller Manager (CCM)

The kube-controller-manager manages internal cluster tasks.

But Kubernetes must also connect to cloud providers for resources.

This includes:

• Load balancers for services

• Storage volumes for persistent data

• Virtual machines for nodes

The cloud controller manager handles this.

The cloud controller manager helps Kubernetes stay cloud-agnostic. It does this by keeping cloud-specific logic separate from the main Kubernetes components. Kubernetes can run on various cloud providers, such as AWS, Google Cloud, and Azure. It can also work in on-premises environments. Best of all, there’s no need to change the core Kubernetes code.

The cloud controller manager connects Kubernetes to the cloud provider’s infrastructure.

We have looked at the control plane components that manage the cluster and make decisions. But the control plane does not run applications itself. The actual applications run on the worker nodes.

So let’s look at the worker node components and see how they work…

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You have more than one AI agent working on the same project:

A few coding agents in your terminal, a chatbot in your browser, an agent deployed on AWS.

Each keeps its “own” memory.

i.e., your terminal agent doesn’t know what you told the browser chatbot 10 minutes ago. And the browser chatbot doesn’t know what’s happening in your editor.

And you copy decisions between chats and carry context1 by hand…

So a single “shared memory” every agent can read from & write to is missing…

I’ll use Omnigraph as the case study for this newsletter.

(It’s an open-source graph engine built for solving this exact same problem.)

Here’s what’s inside this newsletter:

• Why shared folders and vector databases fail. The hidden problem that appears the moment many AI agents write to the same memory.

• Why graph-shaped memory wins. How nodes, edges, and schemas let agents build knowledge instead of disconnected text.

• Object storage without database transactions. How Omnigraph achieves atomic writes on Amazon S3.

• Git for AI memory. Why branches, commits, merges, and policy checks stop agents from overwriting each other or turning mistakes into shared truth.

• One query, three retrieval methods. How graph traversal, keyword search, and semantic search combine to assemble better context for every AI agent.

• When graph memory is the wrong tool. The workloads where a traditional database, a graph database, or a vector database is still the better choice.

Onward.

Why traditional memory breaks

One “shared” memory every AI agent can read from and write to sounds simple.

Yet the obvious ways to build it don’t work well…

Let’s start with two simple approaches:

1. Shared document folder

Every agent stores text in notion, google drive, confluence, or a wiki.

Yet the folder has NO structure an agent can reason over…it’s just a pile of text.

If you ask, “Which deploy touched this service, or who shipped it?”, the agent can answer it off a single file just fine. But it becomes extremely difficult when the answer is spread across 200+ files…

Then agent doesn’t know deploy, service, and person are different concepts. Plus, it has to read everything to be sure it didn’t miss the one file that mattered... or fall back to the classic habit of hallucination2.

A folder stores text,,, NOT knowledge.

2. Vector database

Ask the vector database3 a question using retrieval-augmented generation4 (RAG).

It returns the chunks of text that most closely match your question…This is useful for “find me the passage about X.”

But a vector database holds NO relationships or types.

It can’t start at the customer; find their open tickets, connect them to the engineer on call, and then to the commit that probably caused it. Instead, you get only a bag of similar-sounding snippets, and you need to connect the dots yourself.

So neither one allows an agent to start with one fact and follow it to another related fact…

Even if you ignore these issues, everything falls apart the moment a second agent writes to the same place…

A shared folder has no way to coordinate concurrent writes. If two agents save the same document, the last write wins and the other disappears.

A vector database does NOT distinguish between a proposed change and an accepted one. Once an agent writes new information, every other agent can retrieve it immediately.

i.e., one agent’s mistake becomes every agent’s input (shared context). And agents often get things wrong, so letting one write straight to the shared memory is extremely dangerous.

A “shared” memory for AI agents must do three jobs:

• Read: Assemble context across connected facts. Start from one fact, follow its relationships, and build the context an agent needs.

• Write: Persist new knowledge so it survives after the task ends.

• Coordinate: Allow many agents to read & write simultaneously without overwriting each other or exposing incomplete changes.

Reads and writes aren’t new…but coordination is…

Traditional memory systems assume a few deterministic writers making short, careful, and predictable updates.

But AI agents behave differently:

They reason for minutes, revise earlier conclusions, make ‘mistakes’, and run alongside many other agents touching the same knowledge.

Shared folders & vector databases are NOT designed for this type of workload…

Omnigraph’s core idea is simple:

AI agents don’t need a better folder or a vector database. Instead, a memory system designed for the way they read, write, and coordinate.

I’m happy to partner with Omnigraph on this newsletter.

When dozens of agents share one graph, things get chaotic fast: updates collide, bad writes land immediately, and context drifts.

Omnigraph gives every agent an isolated branch of the same typed graph, so agents can enrich data in parallel while humans deliberately review, approve, and merge changes. Built on Lance, Arrow, DataFusion.

Graph state stored in object storage. MIT.

Check out the repo

Graph based memory

So what should this memory look like?

Imagine facts as dots and relationships as lines connecting them.

• A customer is a dot.

• Each of their tickets is a dot.

• Lines connect the customer to each ticket.

• The engineer who owns a ticket is a dot with a line to it.

• The commit that probably caused the bug is another dot, with a line to the ticket it broke.

This is a graph: dots are nodes & lines are edges.

To answer a question, the system doesn’t search thousands of documents for clues. Instead, it follows the existing edges:

Customer → Ticket → Engineer → Commit 

i.e., four direct hops through connected data.

The graph stores relationships alongside the facts themselves.

This is exactly what shared agent memory has been missing…

Graph schema

A graph alone is NOT enough.

Once many AI agents start writing to the same graph, a new problem appears…

Imagine three agents recording the same production event:

• One writes “deploy”,

• Another writes “deployment”,

• And the third writes “release”.

To you, those are different words with the same meaning…but to the graph, they’re three different nodes.

(Nothing tells the graph they represent the same concept…)

Now when an agent asks, “Show me every deploy this week and who shipped it.”

It follows the deploy nodes and returns only part of the answer. Every deployment and release sits elsewhere on the graph, so the query “silently” never reaches them.

The result looks complete… but it’s WRONG!

The graph didn’t fail,,, but the schema did.

Without a “shared” schema, each agent creates its own vocabulary. The graph stores exactly what each agent writes, even when different terms describe the same concept.

The solution is to define the graph before anyone writes to it.

A schema specifies:

• Which node types are allowed to exist,

• Which relationships connect them,

• How each node is identified.

Every agent writes against the same schema, so they all describe the world in the same way.

So the graph stays consistent, even when hundreds of agents write to it.

Omnigraph schema

In Omnigraph, a schema is a plain text file:

You do NOT write it from scratch.

Instead, the agent generates it from your graph and updates it as your knowledge evolves. And the file describes the graph itself.

Each node defines a type of entity, such as Person, Service, or Deploy. The @key specifies how each node is uniquely identified; for example, a person’s name, a deploy ID, and so on.

Each edge defines a relationship between two node types and the direction an agent can traverse it.

Deploy -> Service literally means “from the deploy to the service it touched.”

Every agent reads the same schema before it writes. This keeps the graph consistent even when many agents update it simultaneously.

So the memory isn’t just a graph…it’s a “typed” graph with a shared schema.

The next question is where this graph lives…

Where memory lives

The graph lives in object storage.

Think of it as a data bucket in the cloud. Some popular examples are Amazon S35, Google Cloud Storage (GCS), and Cloudflare R2.

Modern web apps use object storage for backups, logs, images, videos, and data lakes.

Why Omnigraph chose S3

Omnigraph uses AWS S3 for storage.

Here’s why:

1. Low cost

Amazon S3 storage costs only a few cents per gigabyte each month.

AI agents accumulate knowledge over time. And object storage lets memory grow without the cost of running a large database cluster.

2. High durability

Once data is stored, Amazon S3 replicates it across many availability zones (AZ) within a region to provide very high durability.

This is crucial because the graph becomes your agents’ shared memory and system of record.

3. Shared access

An object storage bucket is NOT tied to one machine.

Every agent with the right credentials can read from and write back to the same bucket. Your coding agent, browser chatbot, and background agents all share “one” memory instead of maintaining separate copies.

i.e., there’s no need to copy state between separate agent data stores.

Yet an object storage has one key limitation:

It stores one object safely at a time, but it can’t commit several objects as a single all-or-nothing operation. A database calls this an atomic write.

Write object A, then B, then C, and the bucket treats them as three independent writes. There’s NO built-in way to say, “Commit all three or none.”

And a graph update almost always touches “many” objects…

So how do you get atomic writes from storage that doesn’t support them?

How atomic writes work

To understand this, you first need to see how Omnigraph stores the graph in the S3 bucket…

How Omnigraph stores the graph

The graph is NOT stored as a single file.

Instead, Omnigraph splits it into many separate stores. This design is what makes atomic writes (all-or-nothing writes) possible.

Take the schema from before:

Each node type & edge type has its own store.

And each store contains every record of that type in versioned files. Under the hood, Omnigraph uses an open columnar file format6 called Lance, yet you never interact with it directly.

So schema above becomes five stores, plus a smaller store called the manifest.

Also, a store is NOT a single file.

Think of nodes/Deploy/ as a folder that keeps growing as new deploys arrive…

(This detail becomes important in a second…)

One update touches many stores

An update to a graph almost always writes to several stores.

This is where object storage turns into a limitation…

Record a single deploy: Alice pushes deploy d-913 to the checkout service.

Omnigraph has to write:

• a new row in nodes/Deploy/ for deploy d-913

• a new row in nodes/Person/ for Alice, if she doesn’t already exist

• a new row in edges/TriggeredBy/ linking d-913 to Alice

i.e., write to three separate stores for one logical update.

And to the bucket, those are just three independent writes…

If the process crashes after writing the Deploy and Person stores but before writing the TriggeredBy edge, the graph becomes inconsistent. Then deploy exists, but nothing says who triggered it…

Besides, object storage can’t roll those writes back because it never knew they belonged to the same logical update.

Manifest tracks live versions

The manifest is the smaller store…It does NOT contain graph data.

Instead, it’s a small table whose only job is to record, for every other store, which version of it is live right now.

It follows these two simple rules:

Rule #1 Stores are immutable

A write “never” modifies a store in place.

A write to nodes/Deploy/ creates a new version alongside the old one. Both versions exist in object storage simultaneously. And the old version remains live (agents keep seeing it) until the manifest says otherwise.

Rule #2 Every store has exactly one live version

The manifest stores a pointer to the current version of each store.

It looks like this:

Now check the deploy from earlier…

The update touches three stores,,, so the manifest has to move three pointers to their new versions.

Publishing a graph update

Here’s the complete (all-or-nothing) write sequence:

1. Omnigraph writes the new store versions without touching the manifest.

2. It creates Deploy v8, Person v12, and TriggeredBy v4 in the bucket.

3. Those versions already exist in object storage, but they’re invisible to agents. Every agent still reads Deploy v7, Person v11, and TriggeredBy v3 because the manifest hasn’t been updated.

4. The final step is to update the “manifest” file.

A single manifest write moves all three pointers at once:

• Deploy → v8

• Person → v12

• TriggeredBy → v4

Thus from an agent’s perspective:

• Before commit: entire old graph is visible (consistent)

• After commit: entire new graph is visible (consistent)

• During commit: there is no partial state (consistent)

The only atomic operation is the update to the manifest file.

If the process crashes before the manifest changes,,, nothing becomes visible. The new store versions remain orphaned files in object storage, while every agent continues reading the previous graph version.

Preventing conflicting updates

What happens if another agent updates the graph before your commit becomes visible?

Omnigraph solves this problem using compare-and-swap7 (CAS).

Before updating the manifest, it checks whether every store is still on the version the writer originally read.

Suppose a writer starts from Deploy v7 and creates Deploy v8.

Before publishing the update, Omnigraph asks one question:

Is Deploy still on version v7?

• Yes. Nothing changed, so the manifest update succeeds.

• No. Another writer already published a newer version. The commit then gets rejected, nothing becomes visible, and the writer retries from the latest state.

Compare-and-Swap simply compares the expected version with the current version before swapping the manifest pointer.

The manifest file is append-only.

i.e., it never modifies older entries. Instead, each successful commit simply adds a new set of live pointers.

CAS prevents one writer from accidentally overwriting changes it never saw.

Proven approach

Omnigraph isn’t the first system to solve this problem…

Apache Iceberg and Delta Lake8 (open table formats) use the same pattern to achieve atomic commits on object storage.

They write new data files first, keep them invisible, then publish them with a single metadata update. It’s a proven design to power production data platforms.

Atomic commits solve one problem:

They guarantee every graph update is complete or NOT visible at all.

But another challenge remains…

What happens when many AI agents want to change the graph at the same time? And how do you stop one bad write from becoming shared memory?

How agents write safely

Compare-and-Swap (CAS) prevents “conflicting” writes:

If two AI agents update the same store at once, one commit succeeds, and the other retries. So the graph remains consistent.

But CAS doesn’t decide whether the write is correct…

AI agents can hallucinate, misread context, or make wrong inferences. If a bad write wins the CAS check, it immediately becomes part of the shared graph.

And then every other agent reads it as fact.

Omnigraph solves this problem using a workflow similar to the Git version control system:

You don’t edit the shared repository. Instead, you create a branch, make your changes, review those, and then merge them. (Omnigraph applies the same idea to graph memory.)

i.e., AI agents don’t write directly to the shared graph, but to branches first.

Let’s dive in!

Every agent gets its own branch

Agents “never” write directly9 to main branch (shared truth).

Instead, each agent creates its own branch from the latest graph and writes there. This branch is private. And the agent can add, modify, or delete data without affecting anyone else’s view. So main branch remains unchanged.

Imagine two agents working on the same graph; each creates their own branch:

They work in PARALLEL without blocking each other, while the main branch remains unchanged…

Branching does NOT copy the entire graph:

A store gets copied only when an agent writes to it for the first time. Until then, the branch shares the same underlying storage as main branch.

Branching is therefore “inexpensive”.

Every merge needs approval

A branch is ONLY a proposal.

It doesn’t become part of main until it’s merged10.

But every merge must be “approved”. You can approve it yourself, or define policy rules that automatically approve routine, low-risk changes from a trusted AI agent (into a scratch branch). Yet larger/riskier changes go through human review.

The key idea is simple:

An AI agent never approves its own changes.

(Let’s return to policy rules later.)

Now, let’s understand what occurs on merge conflicts…

Merge conflicts

Most branches merge automatically.

If two agents change “different” parts of the graph, Omnigraph keeps both changes.

Imagine Agent A updates one deploy, while Agent B adds a new person. Those changes don’t overlap, so the merge succeeds…

Conflicts occur only when two branches modify the “same” field differently:

Suppose Agent A changes deploy d-913 to rolled-back, while Agent B changes the same field to succeeded.

Omnigraph won’t guess which value is correct11. Instead, it reports the exact conflict: node, field, and both competing values. Then a human or a policy rule decides which one wins.

Also graphs introduce another kind of conflict:

One branch can delete a node while another adds an edge pointing to it. And merging both would leave a dangling edge.

Omnigraph detects this conflict before the merge completes, preventing the graph from becoming inconsistent.

i.e., every disagreement becomes an explicit conflict and NOT a silent overwrite of data.

Proven pattern

Omnigraph is NOT the first system to use branches & merges for data.

Dolt brings Git-style branching and merging to SQL databases. lakeFS applies the same idea to data lakes.

Omnigraph extends this pattern to typed graphs, giving AI agents a safe way to propose, review, and merge changes into shared memory.

When many AI agents share the same graph, updates can conflict, incorrect writes can spread instantly, and shared context can drift over time.

Omnigraph gives each agent an isolated branch of a typed graph, allowing agents to work in parallel while humans review, approve, and merge changes before they become shared truth.

Built on Apache Arrow, Lance, and DataFusion. Stores graph state in object storage. Open source under the MIT License.

Check out the repo

Managing graph updates

A safe write still leaves three questions open:

• Who is allowed to make this change?

• Who actually made the change?

• How do you undo the change later?

Omnigraph answers all three with one object: commit.

Every accepted change becomes a commit on main. This commit records who made the change, governs how it entered the graph, and preserves the history to reverse it later if needed.

Authorization layer

Omnigraph itself is the engine—the core that stores the graph, merges branches, executes queries, and manages commits.

You can reach it in three ways:

• over HTTP through the server,

• through omnigraph command-line tool,

• through an SDK embedded in your own app.

AI agents usually connect through a Model Context Protocol12 (MCP) server, which talks to the HTTP server.

It might seem natural to enforce permissions at the HTTP layer. But it protects only one entry point. Plus, the command-line tool and SDK bypass the HTTP server completely.

So Omnigraph enforces permissions inside the engine.

Every write passes through the same authorization check, regardless of whether it came from HTTP, the command line, or an SDK.

There’s NO path to the graph that bypasses the permission system.

What the policy controls

Every authorization check runs against a policy…

Imagine policy as a set of rules that defines who can perform which actions. These are the same policy rules that decide whether a branch can be merged.

The actions are specific:

• read the graph,

• make a change,

• create a branch,

• modify a branch,

• merge into main,

• change the schema.

A policy could allow one actor to merge into main while allowing another to read the graph but never modify it.

Omnigraph writes these rules in Cedar, an open policy language from AWS.

How Omnigraph verifies identity

Every policy rule applies to an actor.

So the first step is identifying who the actor really is. Each actor receives a token when it’s created (similar to a service API key). And every request from the actor includes that token.

The engine then identifies the actor from the token, NOT from information in the request itself.

A request can claim, “I’m an admin.” Yet the token decides whether that’s true…

If the token belongs to a regular actor, the request gets treated as a regular actor every time. Trusting the request rather than the token would allow anyone to claim any identity.

What permissions can control

Omnigraph enforces permissions at the branch level…

A policy can answer questions such as:

• Can this actor13 create a branch?

• Can this actor write to this branch?

• Can this actor merge into main?

Yet it can’t answer more granular questions, such as whether an actor can update one field of a node while leaving every other field untouched. For example, “can this actor edit a deploy’s status without touching its owner?”

(Field-level permissions are on Omnigraph’s roadmap…)

So today the permission system controls access to branches, NOT individual fields.

Commit history

Authorization answers one question:

“Who is allowed to make a change?”

So the next logical question is:

“Who actually made it?”14

Every accepted change becomes a commit on main.

The good news is that commit records:

• who (actor15) made the change,

• when it was committed,

• its parent commit.

Chain those commits together, and you get the history of the entire graph…

Every change has an author, a timestamp, and a place in the commit history.

This chain is the “audit” trail.

Omnigraph does NOT need a separate audit log because the commit history already records every accepted change in order.

Reverting graph updates

Omnigraph never updates data in place.

Every change creates a new version alongside the old one. The manifest simply points to the new version, while the old version remains in object storage.

This is what makes every change REVERSIBLE…

Every historical version of the graph still exists.

You can read the graph exactly as it looked before the merge, inspect what the system knew at that point in time, or compare it with the current state.

Nothing needs to be “reconstructed” because nothing got overwritten…

Rolling back works the same way.

A branch can be discarded, and a merged change can be reverted by creating a new commit that restores an earlier state. The previous versions are still there, ready to be read.

Crash recovery

A write can FAIL halfway through…

New store versions could already exist, while the manifest still points to the previous graph.

The next time Omnigraph opens the graph for writing, it automatically recovers the interrupted operation. It either completes the pending commit or rolls it back.

This recovery gets recorded as its own commit… Yet this commit uses a reserved system identity instead of a human or AI agent, making automatic recovery easy to distinguish in the commit history.

You can immediately tell whether a change came from a human, an AI agent, or the system itself from the audit trail.

How one query searches structure, keyword, and meaning

Every write in Omnigraph gets saved atomically, gets checked before it is shared, and gets protected against crashes & conflicting updates…

Yet an AI agent is only as good as the context it receives.

And the context almost “never” comes from a single lookup:

To answer a question, an agent usually has to assemble related information. Some of it comes from relationships, some from exact keywords, and some from semantic meaning.

The engine has to search all three…

1. Relationships

Start at the LoginService node and follow its edges. Which services depend on it? Which incident involved it? Who owns it?

That’s graph traversal…The engine walks relationships which already exist.

2. Exact keywords

Sometimes you know exactly what you’re looking for:

• the error string ERR_AUTH_443,

• a service name,

• a ticket ID.

That’s full-text search,,, similar to searching a log file with grep.

A graph traversal can’t answer those queries efficiently.

3. Semantic meaning

The agent searches for “can’t log in,” but the relevant incident is titled “authentication failure.”

The words are different… but the meaning is the same…

And that’s what vector search is designed for.

A straightforward solution is to build three separate systems:

• Graph database for relationships,

• Search index for keywords,

• And a vector database for semantic search.

But this approach creates a synchronization problem.

Each system keeps its own copy of the data…

If one index falls behind, they stop agreeing. The graph knows about a node that hasn’t been indexed yet, or the vector database still returns an outdated version of it. Your application now has to query all three systems and combine the results itself.

So Omnigraph takes a different approach…

It stores ONE copy of the graph in object storage and builds three indexes on top of it:

• graph relationships,

• full-text index,

• vector index.

Every index gets built from the same underlying data, so they stay in sync.

Here’s what happens when an AI agent sends a query:

• Omnigraph searches graph, full-text index, and vector index.

• Each search returns its own ranked list of results.

• The engine then combines those lists into one final ranking.

It doesn’t average the scores because each search method uses a different scoring system.

Instead, Omnigraph ranks the results from each search independently, then merges those rankings using Reciprocal Rank Fusion (RRF).

i.e., results that appear near top across many searches rise to the top of final list.

Under the hood, Omnigraph combines several retrieval techniques:

• BM2516 for keyword search,

• Vector k-nearest neighbors17 (k-NN) with cosine similarity for semantic search,

• Fuzzy matching for approximate matches,

• Reciprocal Rank Fusion18 (RRF) for combining results.

You don’t have to know any of these techniques yourself or how to even use them, because the agent using Omnigraph is going to handle it for you…

When to use graph memory

Graph memory solves a specific problem.

Multiple AI agents need to read shared context, write new knowledge, and coordinate those changes without corrupting the shared graph.

Here are some use cases:

• Company knowledge graph where agents read and update decisions, ownership, and relationships.

• Long-term agent memory that survives after each task ends.

• Codebase graph that coding agents continuously read and update.

• Personal knowledge graph managed by one or more AI agents.

The common pattern isn’t the domain…

But multiple agents working on the same knowledge over time.

When NOT to use graph memory

Graph memory is NOT the right tool for every workload…

The biggest limitation is “write” latency.

Omnigraph stores data in object storage. Small writes typically take tens to hundreds of milliseconds, while an in-memory database can respond in well under a millisecond.

It’s a fair tradeoff for AI agents that already spend seconds reasoning…

But it’s NOT a good fit for workloads that require thousands of low-latency writes per second, such as payment processing, click counters, or real-time session state.

Those belong in a transactional database…

You also don’t need graph memory if you only need one retrieval method…

• Need semantic search? Use a vector database to keep it simple.

• Need graph traversal for a single application with one writer? A traditional graph database such as Neo4j is a better fit.

• Need high-throughput transactions? Use a transactional database.

Final thoughts

AI agents turned out to be a new kind of memory user.

They need a memory system built for how they actually read, write & coordinate.

Omnigraph is a typed graph on shared object storage, where every change is proposed, reviewed, and merged before it reaches shared memory.

The agents are already here…And the memory is finally catching up.

Thanks to Omnigraph for partnering on this newsletter.

When dozens of agents share one graph, things get chaotic fast: updates collide, bad writes land immediately, and context drifts.

Omnigraph gives every agent an isolated branch of the same typed graph, so agents can enrich data in parallel while humans deliberately review, approve, and merge changes. Built on Lance, Arrow, DataFusion.

Graph state stored in object storage. MIT.

Check out the repo

Louis & I launched the GENERATIVE AI MASTERCLASS (newsletter series exclusive to PAID subscribers).

When you upgrade, you’ll get:

• Simple breakdown of real-world architectures

• Frameworks you can plug into your work or business

• Proven systems behind ChatGPT, Perplexity, and Copilot

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References

• ModernRelay, Omnigraph

• ModernRelay, Omnigraph Cookbooks

• Apache Iceberg, Table Spec

• Delta Lake, Protocol

• DoltHub, Dolt: Git for Data

• lakeFS, Data Version Control for Object Storage

• Cedar, Cedar Policy Language

• Lance, Columnar Data Format

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How does Netflix stream videos worldwide in milliseconds,,, while some sites take “seconds” to load a basic HTML page?

The answer is proximity…

i.e., closeness between the servers and end-users.

Imagine a single server to handle global traffic:

• International users wait 300ms+ just for the first byte from the “distant” server

• Origin server1 handles all requests from every continent

• One traffic spike could take down the entire site

Onward.

Here’s what you’ll find inside this newsletter:

• Why websites slow down. The hidden costs of distance, overloaded servers, and bandwidth limits.

• Why CDNs became essential. How they evolved from a niche scaling solution into core internet infrastructure.

• What happens when you visit a site. The journey your request takes before content reaches your browser.

• The building blocks of a CDN. Edge servers, origin servers, DNS, Anycast, GSLB, and the components behind global content delivery.

• Where CDNs make the massive impact. The industries and applications that depend on them every day.

• Why CDNs aren’t a silver bullet. The trade-offs, limitations, and operational challenges every engineer should understand.

Read more

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AI agents can use tools1 today.

Model Context Protocol (MCP) provides an agent with a standard way to communicate with databases, APIs, files, and external services.

But there’s a problem MCP doesn’t solve:

What happens when you need many agents to work together?

Onward.

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Building production-ready agentic AI systems that scale across an enterprise is extremely “difficult”.

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Here’s what you’ll learn:

• Scale AI beyond the proof of concept. Build industrial-scale systems with classical and generative AI.

• Fix the data problem. Power AI with governed, high-quality data.

• Make better architecture decisions. Choose the right databases for AI applications.

• Streamline deployment. Discover, procure, and deploy AI solutions with AWS Marketplace.

Watch the AWS panel for free right now and see how enterprise leaders are building AI systems with speed, governance, and control.

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(Thanks to AWS for partnering on this post.)

I want to introduce Eric Roby as a guest author.

He’s a senior backend and AI engineer focused on building real-world systems and teaching developers how to do the same. He runs the YouTube channel codingwithroby, where he focuses on scalable backend architecture and integrating AI into production applications.

Through his content and courses, he helps engineers go beyond tutorials, think in systems, and develop the skills that actually matter for senior-level roles.

Find him on:

• LinkedIn

• Substack

Here’s what’s inside this newsletter:

• Why MCP wasn’t enough. The missing coordination layer that prevents AI agents built by different vendors and frameworks from working together.

• How AI agents actually collaborate. Agent Cards, task lifecycles, delegation, streaming, discovery, and the protocol that standardizes agent-to-agent communication.

• The emerging AI interoperability stack. How A2A, MCP, RAG, AGNTCY, ACP, and AP2 fit together, and why the industry is converging around open standards.

• Where A2A breaks down in production. Latency, trust, authorization, debugging, observability, and the engineering tradeoffs you’ll face when building multi-agent systems.

• A practical guide to building with A2A. Security, Agent Card design, interoperability, production architecture, and the patterns that make agent ecosystems reliable at scale.

Get Instant Full Access Now

(Golden members get all letters like these!…)

💡 Quick refresher

AI agent is a system that uses a large language model2 (LLM) to reason, plan, and take actions on your behalf. It’s not just answering questions. But also calling tools, checking results, and deciding what to do next. Think of it as an LLM with hands.

A single agent can be brilliant at one job…

They can write code, analyze data, and schedule meetings. But real work crosses domains. And you need one agent to code, one to test, and one to ship. If you stack all of this into one mega-agent, it becomes slow, expensive, and brittle.

i.e., specialization wins, just as it does on a real team…

But then, what is Agent-to-Agent (A2A) protocol?

Can’t agents already communicate through normal API calls?

The problem is every agent-to-agent integration becomes custom: one agent needs to know what another agent can do, what inputs it expects, how to authenticate, how to hand off a task, how to track status, how to pass context, and how to receive the final result.

A2A is an emerging open standard for the coordination layer…

It gives agents a shared way to advertise their capabilities, accept tasks, exchange messages, report progress, and return outputs.

i.e., A2A standardizes the workflow in which one agent delegates work to another.

Imagine A2A as the next connection layer:

• TCP/IP3 lets machines talk to each other

• HTTP lets documents link to each other

• APIs let services compose into systems

• MCP lets agents connect to tools

• A2A does the same thing for agents

Google launched A2A in April 2025.

(They partnered with 50+ tech companies and service providers. 150+ organizations joined them later.)

To understand why A2A matters, you need to understand the pain it solves…

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AI Agent Collaboration Problem

AI agents are exponentially growing…

Every team, every vendor, every platform is shipping them…but each one is a SILO.

Your customer relationship management (CRM) agent can’t coordinate with your internal data agent. Your coding agent can’t delegate to a testing agent built by a different team. They might all live in the same company, in the same building, on the same network…

Yet they still can’t work together.

Framework4 lock-in is part of the problem.

An agent built with LangGraph can’t directly communicate with one built with CrewAI, Google’s ADK, or AutoGen. Each framework has its own internal orchestration model. While cross-framework collaboration requires custom glue code, written and maintained by you.

That's where A2A comes in.

Now let’s look at what it is…

What A2A Actually Is

Here’s a simple analogy for you:

Think of A2A like hiring a contractor. You don’t need to know what tools are in their truck or how they manage their schedule. You hand them a job; they confirm what they can do, and then hand back the finished work. A2A is the standard contract format. This lets any contractor work with any client. They don’t need to renegotiate from scratch each time.

The Agent2Agent (A2A) protocol is an open standard.

It helps AI agents find each other’s skills5. They can send messages, delegate tasks, and work together safely. This all happens NO matter their framework, vendor, or platform.

Here are five design principles on which the A2A v1.0 specification is built:

• Simple. Reuse existing, well-understood standards. HTTP, JSON-RPC 2.0, Server-Sent Events. No new transport layer to learn.

• Enterprise Ready. Address authentication, authorization, security, privacy, tracing, and monitoring by aligning with established enterprise practices.

• Async First. Designed for long-running tasks and human-in-the-loop interactions. Quick queries to deep research that takes hours or days.

• Modality Agnostic. Support text, audio, and video via file references, structured data, and forms, and embedded UI components.

• Opaque Execution. Agents work together by sharing their skills and information. They don’t need to reveal their internal thoughts, plans, or how their tools work.

The last one is the most important.

A2A treats agents as autonomous, opaque entities. You don’t need to share your agent’s internals to participate in the ecosystem.

Now let’s open the hood…

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How A2A Works Under the Hood

Here are the key points you should understand:

• How agents connect,

• What they share,

• And how they communicate.

Let’s dive in!

Client-Server Model and Agent Discovery

Every A2A interaction has two roles:

• Client agent is the requester. It discovers remote agents, sends tasks, and coordinates the workflow.

• Remote agent is the executor. It receives and processes tasks, does the work, and returns results.

Any agent can play both roles, depending on the workflow.

A coding agent is a client when it delegates testing to a QA agent. The same coding agent becomes a remote agent when a project manager agent asks it to write code.

Agents discover each other through an Agent Card.

This JSON metadata document is published by every A2A server6. It describes the agent’s identity, capabilities, skills, endpoint URL, supported data formats, and authentication needs.

Agent Cards live at a standard, well-known URI: /.well-known/agent-card.json.

The path follows RFC 86157. This is the same way browsers find SSL verification files. A client agent checks another agent’s card. It does this to see what the agent can do before sending requests.

This is like MCP’s capability negotiation8, but for agents instead of tools.

Here’s what an Agent Card includes:

• Name, description, version

• Service endpoint URL

• Supported input and output modalities, as MIME types

• Skills, with IDs, descriptions, tags, and examples

• Authentication requirements, aligned with OpenAPI security schemes

• Supported A2A features like streaming, push notifications, and extended cards

Agents can also serve an Extended Agent Card behind authentication.

This lets you hide sensitive skills or internal capabilities from unauthenticated discovery. i.e., public-facing summary first, full details after you prove who you are.

Tasks, Messages, and Artifacts

Communication in A2A is task-oriented.

When a client agent sends a message, the remote agent creates a Task. This is the fundamental unit of work in A2A. Plus, every task has a unique ID and progresses through a defined lifecycle.

Here are 9 possible lifecycle states:

• submitted: task received by the remote agent

• working: agent is actively processing

• input-required: agent needs more information from the client

• auth-required: agent needs credentials or authorization to proceed

• completed: task finished successfully (terminal)

• failed: task could not be completed (terminal)

• canceled: task was canceled by the client (terminal)

• rejected: agent decided not to perform the task (terminal)

• unknown: state recovery scenario

This is fundamentally different from a simple function call.

Tasks are stateful and can involve many message exchanges. They can also pause to ask the client for more information…Or pause to request authorization before doing anything destructive.

This is the kind of back-and-forth a real collaboration requires…

• Messages are the units of communication between agents. Each message has a role (user or agent) and contains one or more Parts.

• Parts are the smallest content units. Text parts hold plain text. While File parts hold files or images via URI or inline bytes. And Data parts hold structured JSON. This supports rich, multimodal communication without forcing everything into text.

• Artifacts are the outputs of a task. Documents, images, structured data, or any other deliverable remote agent produces. Artifacts, like messages, consist of Parts. They can be streamed as they are created.

• Contexts group related tasks together via a contextId. This provides continuity across a series of interactions, similar to a conversation thread.

Communication Patterns and Protocol Bindings

Before we get into transport details, there are two orchestration patterns to understand:

• Centralized orchestrator. One lead agent owns the plan. It discovers specialist agents, delegates tasks, and stitches the results back together. This makes it easier to reason about and debug. The default pattern for most enterprise workflows today. This is the “tech lead delegates to ICs” model.

• Decentralized swarm. No single orchestrator. Agents find each other directly and share tasks. Each agent chooses whom to contact next based on its own judgment. More flexible. More resilient. Harder to trace. Closer to a marketplace or a team of peers.

A2A supports both.

It’s a protocol, not an orchestration model. The protocol just defines how any two agents talk. The topology is your choice. Most real systems begin centralized. They switch to swarm patterns9 when they can see well and have trust systems to handle complexity.

Plus A2A supports three interaction patterns at the network level:

• Synchronous request-response. Client sends a message and waits for a complete response. Good for quick tasks.

• Streaming via Server-Sent Events. Server streams incremental updates in real-time. Good for tasks where you want to show progress.

• Push notifications. Server sends async updates to a client webhook10. Good for long-running tasks where the client doesn’t want to hold a connection open.

And for the actual wire protocol, A2A v1.0 supports three bindings:

• JSON-RPC 2.011: original and most commonly used binding. Methods include message/send, message/stream, tasks/get, tasks/cancel, and others.

• gRPC: added for high-performance scenarios. Uses Protocol Buffers for message serialization.

• HTTP REST: uses standard HTTP verbs and URL patterns like /v1/message:send and /v1/tasks/{id}. Lowers the barrier for teams that prefer REST over JSON-RPC.

All three bindings share the same semantic model and abstract operations.

The choice of binding is a deployment decision, not a design one. As of v1.0, the protocol’s main data model is in Protocol Buffers12 (a2a.proto). This file is the single source of truth. All bindings derive from this definition.

This familiarity with existing web standards is intentional.

A2A works with your current enterprise setup. It connects easily to load balancers, API gateways, and observability tools. No new tools are needed!

Now that you understand how A2A works, the obvious question: how does it relate to MCP?

A2A vs MCP: Complementary, Not Competing

Read more

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I’ve been using Claude Code for more than half a year now…daily, for actual work.

For most of the time, I ignored a hidden folder that controlled everything Claude did.

Every session, I copied the same instructions: setup, preferences, and what to avoid1. And Claude kept asking permission for things I’d already approved. So when I opened a new project, it knew nothing.

None of it was necessary…

Claude Code2 is Anthropic’s AI in your terminal.

It reads code, writes files, and runs commands. When you install it, a hidden folder appears: .claude. This folder controls everything: Claude’s instructions, permissions, reusable commands, and session memory3. Configure it right, and Claude walks into every project already knowing the rules.

With the launch of Cowork4, there’s another folder on your machine doing similar work. Same idea, but a different surface. Claude keeps your routines, outputs, and memory5 outside your conversations.

This newsletter gives you a complete map: what’s inside both systems, what each file does, and how it all fits together…

Onward.

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Cohorts group related files, and chunks so you review one idea at a time. Layers order them so foundational changes - data shapes, contracts - come before the code that depends on them. Code Peek lets you click any variable, function, class, or type to see its definition and usages without leaving the tab, while Semantic Diff view cuts past formatting noise to show what actually changed.

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I want to introduce Ilia Karelin as the guest author.

He comes from a data background, data science, analytics, and BI, and brings that analytical mindset to Prosper In AI, his newsletter, where he constantly experiments with AI tools and workflows to help professionals adopt AI in ways that actually improve their work and lives.

Follow him on LinkedIn as well.

Here’s what you’ll find inside this newsletter:

• The hidden operating system behind Claude Code. What every file in .claude/ actually does, why CLAUDE.md is only one piece of the puzzle, and how Claude decides what to load before you type a word.

• Configuration layer nobody explains. Instructions, permissions, commands, hooks, memory, and MCP servers--what each one controls and where they fit in the stack.

• How Claude really works under the hood. The startup sequence, memory system, permission checks, tool pipeline, and why understanding this flow makes debugging dramatically easier.

• Claude Code vs Claude Desktop vs Cowork. Where configuration lives, what carries across sessions, what stays local, and why the same workflow behaves differently on each platform.

• The mistakes that quietly break Claude. Bloated CLAUDE.md files, stale memory, overpowered permissions, weak commands, broken hooks, and the patterns that make Claude ignore your rules.

Get Instant Access

What lives inside

Every file inside the folder falls into one of three categories: instructions, permissions, or reusable commands.

Open a Claude Code session, and this folder is what it reads first. Only then does it act…

💡 Quick note for context:

• Claude Desktop is the native app: conversational, no file access.

• Cowork6 is the visual workspace: live artifacts, local folders, and autonomous runs.

Yet neither uses the .claude/ folder the way Claude Code does.

They follow the same philosophy: persistent context, reusable actions, and behavior controls. But files live in different places and are managed differently.

(I’ll cover this later.)

Now we know what’s inside, let’s look at exactly where everything lives…

The folder map: what lives where

Here’s the first thing that trips people up…there are two .claude folders:

• ~/.claude/ lives in your home directory (/Users/yourname/.claude). This is your “global” Claude configuration. It applies to every Claude Code session you run, across all projects.

• .claude/ lives at the root of a specific repository or working directory (inside a project). Everything here applies only when you’re working inside the project.

Think of these 2 folders as layers…

Claude loads the global config first, then the project config on top of it. i.e., project settings “override” global ones where they conflict.

Here’s what’s in the global folder:

And here’s what you’ll find inside the project folder:

NOTE: CLAUDE.md does NOT live inside .claude/ folder. Instead, it sits at the root of your project.

Think of it this way:

• .claude/ stores Claude’s settings and behavior rules7

• CLAUDE.md stores instructions and project context

They work together, but they are separate files…

Plus, .mcp.json lives at the project root.

Model context protocol (MCP)8 servers allow Claude to connect to external tools such as databases, APIs, and services.

• If you want MCP tools available everywhere, configure them globally in ~/.claude/settings.json.

• But if you want MCP tools for just one project, use .mcp.json inside the project instead.

This matters because different MCP setups can make Claude behave differently across projects.

You can commit the project .claude/ folder to git. i.e., everyone on your team gets the same Claude setup when they clone your repository.

You should commit files like:

• settings.json

• commands/

But do NOT commit settings.local.json. It stores machine-specific paths and personal settings that usually break on other machines.

Claude used to support CLAUDE.local.md for personal instruction overrides… But it’s now deprecated. Today, the recommended approach is to use @ imports inside your main CLAUDE.md.

Also, rules/ helps you split a large CLAUDE.md into smaller files by topic.

For example:

• architecture.md

• mistakes.md

• conventions.md

Claude automatically reads every .md file inside rules/ when a session starts. No imports or extra setup needed. You just add the file, and Claude uses it.

You can also make rules apply only to certain folders.

For example, rules/api-conventions.md can load only when Claude works inside src/api/.

This helps when different parts of the project follow different rules…

TAKEAWAY

Commit these files:

• CLAUDE.md

• .claude/settings.json

• .claude/commands/

• .claude/rules/

So when a new engineer clones the repo, Claude already knows the project rules, restricted files, and available commands.

But do NOT commit .claude/settings.local.json. Instead, add it to .gitignore because it stores personal settings. It works only on one machine.

With the folder structure mapped out, let’s dig into the most important file: CLAUDE.md.

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CLAUDE.md: instructions layer

CLAUDE.md is not documentation for humans, but instructions for Claude.

Regular docs explain the project to developers: what the app does, how to install it, and how the API works. While CLAUDE.md tells Claude how to behave on a project: what rules to follow, what conventions matter, and what files to avoid.

When you start a session, Claude loads every CLAUDE.md file it can see, in this exact same order:

1. ~/.claude/CLAUDE.md - your global instructions

2. ./CLAUDE.md - instructions for the current project

3. Any CLAUDE.md files inside subfolders you are working in

They stack together. i.e., each file adds more instructions without replacing the earlier ones.

You can also split large instructions into separate files using @ imports. For example, @docs/architecture.md. Claude will automatically load the file at the start of the session.

This keeps your main CLAUDE.md short while still giving Claude access to deeper project details.

IMPORTANT: imported files still count toward Claude’s context limit. Splitting files improves organization, but it does not reduce the amount of context Claude carries into the session.

Here’s what belongs in CLAUDE.md:

1. Architecture rules Claude cannot infer

Some rules are invisible in the code itself.

For example:

• “Never import directly from utils.ts.”

• “Always use the shared API client.”

• “Do not edit generated files.”

Claude will NOT reliably discover these patterns on its own... So write them down.

2. Environment and workflow context

Include the information a new engineer would spend days piecing together:

• which test runner you use

• how deployments work

• where database configs live

• what setup steps usually fail

• which scripts matter

3. Hard boundaries

Tell Claude what it should never touch without permission.

Use strong markers like:

• IMPORTANT

• YOU MUST

Anthropic has confirmed emphasis markers improve adherence to instructions.

What does not belong

Do not fill CLAUDE.md with:

• long essays

• duplicated documentation

• theory

• things already obvious from the code

Here’s a simple rule:

If removing a line would not change Claude’s behavior, delete it.

CLAUDE.md has no hard length limit. Yet longer files weaken instruction reliability. As the file grows, instructions compete for attention9. And rules near the bottom become easier to ignore than rules near the top…

A 200-line CLAUDE.md is usually not thorough and noisy.

Here’s a tiny but effective CLAUDE.md:

# Project Context

This is a personal finance tracker.
Users log expenses; the app categorizes them.
Run locally with: `npm start`. Tests: `npm test`.

## Rules

- IMPORTANT: never store real financial data in test files
- All database queries go in /db - not scattered across other files
- Don’t touch the /legacy folder - it’s kept for reference only

## Common tasks

- Adding a new feature: check /docs/architecture.md first
- Running into a bug: look in /logs before changing any code

Claude already knows what the project does, what files to avoid, and where to look before changing anything.

Here’s the fastest way to start: run /init inside a Claude Code session. Claude scans your project, detects things like the build system and test framework, then generates a starter CLAUDE.md for you.

That is usually much better than starting from a blank file.

One distinction matters here: prompts and configuration are not the same thing.

Use prompts for one-time requests:

• “Explain this simply.”

• “Summarize this as bullet points.”

You can use CLAUDE.md for rules that should apply every session:

• coding conventions

• hard boundaries

• project context

• workflow rules

Here’s a simple test to help you: If you repeat an instruction in every new session, then put it in CLAUDE.md.

Instructions are only half the picture. Now let’s look at the skills and commands that make Claude reusable…

Skills and commands: reusable workflows

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The following is the third of a premium 3-part1 newsletter series… where everything comes together for mobile system design: architecture, how the platform behaves, and what happens after you ship.

On with part 3 of the newsletter:

===

In this newsletter, we’ll learn 14 concepts that shape how production apps are built and operated:

40. Modular Architecture/Feature-Based Modules,

41. Backend for Frontend (BFF) for Mobile,

42. App Versioning & Backward Compatibility,

43. Feature Flags & Remote Configuration,

44. Deep Linking for Navigation & Growth,

45. Navigation & Screen Flow Architecture,

46. Permissions Model,

47. Accessibility (a11y),

48. Localisation & Internationalisation (i18n),

49. Graceful Degradation,

50. Geo Location Usage,

51. Crash Reporting & Performance Monitoring,

52. Crash-Free Rate as an SLI,

53. Staged Rollouts & Safe Releases.

For each concept, you’ll learn:

• What it is and how it works

• Real-world example

• Tradeoffs

• Why it matters for mobile

Onward.

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I want to reintroduce Shefali Jangid as a guest author.

She’s a web developer, technical writer, and content creator with a love for frontend architecture and building things that scale.

Check out her work and socials:

• Shefali.dev

• GitHub

• Twitter

You’ll often find her writing about web development, sharing UI tips, and building tools that make developers’ lives easier.

Architecture

This section explains different architectural patterns and design approaches that help build apps that are modular, easier to maintain, and can grow without becoming difficult to update.

Let’s dive in!

40. Modular Architecture/Feature-Based Modules

As mobile apps grow, the codebase can become very large and difficult to manage.

If everything is written in one BIG project, it becomes harder to understand the code, make changes, and test new features.

To solve this problem, you can use modular architecture.

In modular architecture, app is divided into separate modules, where each module represents a specific feature of the app.

An app could have modules like:

• Auth module: Handles login and signup.

• Feed module: Shows posts or content.

• Checkout module: Handles payments and orders.

Each module contains its own code and exposes only the parts other modules need.

This keeps features separate and prevents different parts of the app from interfering with each other.

This structure makes the app easier to understand and maintain.

Why It Matters

In a very large app, building the entire project can take a long time…

If a single codebase contains hundreds of thousands of lines of code, compiling the app could take several minutes.

With modular architecture, only the module that changed needs to be rebuilt. This makes builds much faster and helps you work more efficiently.

Real-World Example

Airbnb, Uber, and Spotify use modular architecture in their mobile apps.

They adopted this approach to accelerate development, improve code organization, and allow different teams to work on separate features simultaneously.

Trade-offs

Modular architecture also introduces some challenges.

You need to carefully design the boundaries between modules and define clear interfaces for how modules communicate with each other. Some shared functionality, such as analytics, navigation, or design systems, may need to live in a common core module that all features can use.

It can also be difficult to convert an existing large app into a modular structure after it has already grown.

Practical Tip

It’s useful to visualize how modules depend on each other.

Tools like dependency graph visualizers can help teams see how modules are connected and identify problems such as circular dependencies, where two modules depend on each other. This usually means the architecture needs improvement.

While modular architecture improves how the app code is organized, mobile apps also depend heavily on backend systems designed specifically for them.

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41. Backend for Frontend (BFF) for Mobile

Mobile apps often need data from “many” different backend services.

A single screen requires data from a user service, a recommendation service, and a content service.

If the mobile app directly calls all these services, it would need to make several network requests and combine the results on the device. This can SLOW down the app and increase battery usage.

To solve this, use a technique called Backend for Frontend (BFF).

A BFF2 is a special backend layer designed specifically for a specific type of client, such as a mobile app.

Instead of the mobile app calling many services, it sends one request to the BFF. The BFF then communicates with the required services, gathers the data, combines it, and sends back a single optimized response.

This makes the mobile app simpler and reduces the number of network requests.

Why It Matters

Every network request adds delay & consumes battery.

If a mobile screen requires data from five services, the app will make five separate API calls. This increases latency and slows down the user experience.

With a BFF, the mobile app only makes one request, and the server handles the rest. This improves performance and keeps the app more efficient.

Real-World Example

Netflix uses the BFF approach to serve different types of clients.

They maintain separate backend layers for iOS apps, Android apps, TVs, and web browsers.

Each BFF returns data formatted specifically for that device.

A mobile BFF sends smaller responses optimized for mobile screens, while the TV version may include higher-resolution metadata.

Trade-offs

Using a BFF also introduces an additional service that needs to be maintained.

It is important BFF does not become overly complex. Its main responsibility should be to aggregate and transform data for the client. Core business logic should remain in the backend services that actually own the data.

As backend APIs evolve, another challenge appears: users often run older versions of the app...

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42. App Versioning & Backward Compatibility

When mobile apps are released, users do NOT always update them immediately.

Some people update right away, but others may continue using an older version of the app for weeks or even months.

So the backend system must support many versions of the app simultaneously.

i.e., server should continue to work with older app versions while also supporting the latest version.

To manage this, use versioning:

Versioning assigns each API, or app release, a version number so the server knows how to communicate with different app versions.

Check these API endpoints:

/v1/users
/v2/users

Each version can return data in a format that matches what that specific app version expects.

Why It Matters

If the backend changes something in the API without considering older app versions, older apps will suddenly stop working…

If the server removes a field that an older app depends on, the app could crash or show broken screens.

Unlike websites, mobile apps cannot be updated instantly for every user. This is why systems must remain backward compatible.

Real-World Example

Stripe uses API versioning to maintain compatibility.

When a client makes an API request, it specifies which API version it expects. Even if Stripe changes the API later, older clients still receive responses in the format they were built for.

This allows the platform to evolve without breaking existing apps.

Trade-offs

Supporting many versions of an API requires extra maintenance.

You need to keep older fields or response formats in the system for some time.

A common strategy used to manage this safely is called the Expand–Contract pattern:

1. First, add new fields or features without removing the old ones.

2. Gradually update apps to use the new fields.

3. Once all users have updated, remove the old fields.

This approach prevents breaking older versions while still allowing the system to evolve.

Even when APIs remain backward compatible, you still need safe ways to release new features…

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43. Feature Flags & Remote Configuration

When you add a new feature to a mobile app, you must release a new version of the app and wait for users to update it.

This process can take time because apps must go through the app store review, and not all users update their apps immediately.

Feature flags solve this problem:

A feature flag is a switch that lets you remotely enable or disable a feature without releasing a new version of the app.

The feature’s code is already included in the app, but it stays disabled by default. You can later enable it from a dashboard for specific users or for everyone.

This allows you to control when features are released.

Remote configuration extends this idea even further. It allows you to change certain app behaviors remotely, such as:

• UI text or labels

• Feature limits

• Layouts or design elements

• Experiment settings

Tools such as Firebase Remote Config are commonly used for this.

Why It Matters

Submitting an app update to the app store can take up to 3 days for approval.

If a new feature has a bug and has already been released to millions of users, fixing it through another app update can take time.

With feature flags, you can disable the feature instantly from a dashboard without waiting for another release.

This makes apps safer and easier to manage in production.

Real-World Example

Many companies use feature flags to run A/B testing3 experiments.

Airbnb tests two different versions of a feature:

• One group of users sees the original version.

• Another group sees a new version.

Feature flags control which users see which version, allowing the company to measure which design performs better.

Trade-offs

While feature flags are powerful, they can also create complexity.

If too many flags are added and never removed, the codebase can become cluttered with outdated flags. To avoid this, you should define a lifecycle policy where every feature flag has a purpose and an expiry date. Once the feature becomes permanent, the flag should be removed from the code.

Feature flags help control when features are released, but navigation systems determine how users actually reach those features inside the app.

44. Deep Linking for Navigation & Growth

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When people talk about Artificial Intelligence (AI), they often refer to models such as Generative Pre-trained Transformers (GPTs), Large Language Models (LLMs), and neural networks.

It sounds like magic, software that thinks…

But here’s the uncomfortable truth:

The biggest breakthroughs in AI are not coming from better models; they’re coming from better infrastructure.

If AI were only about the models, there would be no need for huge, warehouse-sized data centers packed with specialized hardware and powered by massive amounts of electricity.

So what’s actually going on inside those buildings?

To make sense of everything that follows, we need a simple mental model.

Let’s find out one layer at a time…

Onward.

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I’d like to introduce Ashish Prajapati.

He is an AI infrastructure specialist and technology educator who helps engineers understand the technologies powering modern AI.

With expertise spanning data centers, cloud computing, GPU infrastructure, Generative AI, and Agentic AI, he focuses on turning complex concepts into practical, real-world knowledge.

His training programs have reached over 39,000 learners worldwide, and through his free mentoring initiative, Become a Solutions Architect (BeSA), he has helped over 23,000 professionals accelerate their careers.

As AI adoption continues to grow, understanding the infrastructure behind AI systems is becoming a valuable skill for architects, engineers, and IT professionals.

For a limited time, this newsletter's readers can use code SYSTEM-DESIGN to get 25% off Ashish’s live NCA-AIIO bootcamp.

This intensive one-day workshop covers the fundamentals of NVIDIA AI infrastructure, GPU networking, accelerated computing, and AI operations, and prepares you for the NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO) certification.

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Here’s what you’ll find inside this newsletter:

• The hidden architecture behind AI. Why models are only the visible tip of a much larger infrastructure stack.

• The physics that limit AI growth. How power, cooling, and space shape every AI data center.

• Why GPUs became the backbone of AI. The architectural advantages that made modern AI training possible.

• The real bottlenecks in modern AI. Why memory, networking, and coordination determine performance at scale.

• How thousands of GPUs work together as one system. The software, networking, and orchestration layers that enable large-scale AI.

Think of an AI Data Center as a Factory

Before we go deep, build this mental model; it will explain almost everything that follows:

• AI Data Center = Factory

• Data = Raw material

• Graphics Processing Units (GPUs) = Machines on the factory floor

• Network = Internal transport system

• Storage = Warehouse holding raw materials

• Software = Assembly line logic

• Output = Trained intelligence

Now imagine a car factory.

It doesn’t just need skilled workers; instead, it needs conveyors, warehouses, and power. Remove any of those, and production stops.

An AI data center works the same way…

AI infrastructure processes vast amounts of data using mathematical operations, similar to how a factory transforms raw materials into finished goods.

Remove any one ingredient, and the entire system stalls…

Once you see AI as a factory, the next question becomes obvious: what limits how fast this factory can run?

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First Problem: Physics

Before AI is a software problem, it’s a physics problem…

Here are three hard physical constraints that shape every design decision in an AI data center:

1. Power

Modern AI workloads rely on high-density GPUs that consume enormous amounts of electricity.

A single server rack of GPUs can draw 80-120 kilowatts. i.e., roughly equivalent to 40 homes running at full load simultaneously. Yet you can’t just add more GPUs forever.

You’re limited by how much electricity your facility can physically receive and distribute.

2. Cooling

More power means more heat, and heat is the enemy…

Here’s what happens if you don’t remove it fast enough:

• Hardware fails prematurely,

• Performance throttles back to avoid damage,

• In extreme cases, systems shut down entirely.

To manage the intense heat generated by AI workloads, modern data centers are increasingly adopting liquid cooling, which is far more effective than conventional air cooling.

Coolant is circulated through cold plates attached directly to GPUs and CPUs, absorbing heat much more efficiently than air. The warmed liquid is then pumped to heat exchangers, where the heat is removed before the coolant is recirculated.

This approach enables higher rack densities, lower cooling power consumption, and more consistent performance for large-scale AI training and inference1 workloads.

3. Space

Even if you had unlimited chips and unlimited power, you still need racks to mount them and floor space to put those racks.

Scaling AI is not just software--it’s real estate, energy infrastructure, and thermodynamics combined.

Takeaway

A data center is, at its core, a power plant combined with a massive, precisely controlled air-conditioning system. Every engineering decision flows from managing this reality.

After dealing with the physical challenges, the next question is: how we perform the computation?

Compute: Why GPUs Took Over

To understand why GPUs dominate AI, picture a very simple task…

Imagine you have 1,000 fence posts to paint:

• Option A: One expert painter, working post by post. Slow, but the painter can handle any unexpected situation--a broken post, a curved section.

• Option B: 1,000 workers, each painting exactly one post simultaneously. Extremely fast, but each worker knows only one simple task.

That’s the difference between a CPU and a GPU.

CPU (Central Processing Unit)

• A few very powerful cores

• Handles complex, varied, sequential tasks

• Acts as the “manager” making decisions and coordinating work

GPU (Graphics Processing Unit)

• Thousands of smaller, simpler cores

• Handles same simple operation repeated millions of times, all at once

• Acts as the “factory workers” doing mass production

AI training repeatedly performs matrix multiplications billions of times, making minor adjustments after each step. GPUs are specifically designed for this type of work.

DPU (Data Processing Unit)

• As AI systems grow, a third processor becomes important.

• DPU handles networking, storage I/O, and security operations--tasks that would otherwise steal cycles from the GPU and CPU.

Here’s the system view:

• CPU = manager (decision-making)

• GPU = factory workers (mass production)

• DPU = logistics and operations (keeping materials moving and systems secure)

But even with powerful GPUs, computation alone isn’t the limiting factor; the real challenge is keeping them fed with data…

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Hidden Bottleneck: Memory and Storage

Here’s something that surprises most people:

GPUs don’t fail because they’re slow. They fail because they’re hungry—constantly starved for data.

Memory Hierarchy

Data doesn’t live in one place. It moves through a layered system, trading speed for capacity:

• GPU Memory (HBM2): Blazingly fast but tiny; usually tens of gigabytes. This is the GPU’s working memory.

• CPU RAM: Larger (hundreds of gigabytes), but slower than HBM.

• SSD/NVMe3 Storage: Enormous capacity at a lower cost, but still slower.

• Network/Object Storage: Nearly unlimited, but the slowest link in the chain.

Every time the GPU needs data that isn’t already in its fast local memory, it must wait... And a GPU sitting idle costs just as much money as one doing useful work.

Takeaway

AI performance is not determined solely by how fast the GPU computes. Instead, by how fast data can be fed into the GPU.

A faster chip with a slow data pipeline is like a faster car stuck in traffic.

Tiered storage in practice

In real AI data centers, storage is organized into tiers that roughly map to hot, warm, and cold data:

• Hot: NVMe SSDs holding active training datasets and model checkpoints.

• Warm: Parallel file systems4, such as Lustre, General Parallel File System (GPFS), provide high-speed shared access across many GPUs.

• Cold: Object storage, such as Amazon Simple Storage Service (Amazon S3), for storing raw datasets and older checkpoints.

As models grow larger, a single GPU is no longer enough…

Techniques such as model sharding5 and distributed training6 distribute the model across multiple GPUs and memory layers.

And once data starts moving across machines, a new bottleneck appears: how those machines communicate with each other.

Networking: Bottleneck Nobody Talks About

When training a large AI model, you typically use thousands of GPUs working together rather than a single GPU…

And those thousands of GPUs must constantly communicate with each other, sharing what they’ve learned at every step…

Every second a GPU waits for data or synchronization is a second of lost productivity and wasted investment.

Cluster topology and fabrics

Modern AI clusters typically use leaf-spine network architectures7: a design built for high throughput and low latency at scale.

Every GPU server can reach every other GPU server in at most ‘two’ hops.

Within a node, GPUs talk over NVLink (intra-node fabric). Between nodes, traffic flows over InfiniBand or RDMA over Converged Ethernet (RoCE), the inter-node fabric8.

There’s also a clear speed hierarchy:

• NVLink9: fastest path, a direct, private highway connecting GPUs within the same server.

• InfiniBand/RoCE10: very high-speed interconnect between separate servers. Imagine it as the “bullet train” of the data center.

• Ethernet: general-purpose, flexible, and ubiquitous, but not designed for AI’s most extreme bandwidth and latency demands.

Amdahl’s Law: Why More GPUs Aren’t Always Better

There’s a fundamental principle in distributed computing called Amdahl’s Law.

In plain English: if parts of your system must wait for other parts, adding more workers eventually stops helping.

In AI workloads, adding more GPUs increases computing power but also increases communication overhead. As GPUs spend more time sharing and synchronizing data, the bottleneck gradually shifts from computation to communication.

Smarter data movement

Modern AI systems address this with several techniques:

• Remote Direct Memory Access (RDMA): Provides low-latency, high-throughput communication by enabling direct memory-to-memory data transfers between servers.

• RDMA kernel11 bypass: Lets the network adapter access application memory directly from user space12, eliminating unnecessary operating system overhead.

• GPUDirect: GPUs can read from and write to storage or network cards directly, bypassing the CPU entirely.

• Zero-copy pipelines: Data takes the shortest path from source to GPU, with no redundant copying along the way.

The goal of AI networking is to make thousands of GPUs behave like one giant, unified processor, as if the data never had to travel at all.

Once communication is in place, the next challenge is dividing the work efficiently across all those GPUs…

Read more

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How do you keep up with agentic engineering when a new tool ships every week?

You do not…

You learn the ideas behind the tools, and let the tools come and go.

The pace will not slow down. New models, new frameworks, a new “this changes everything” launch every few days. Chase all of it, and you spend more time switching tools than using them.

But underneath the noise, it is the same few ideas every time.

One calls it a skill, the next calls it a rule; it's the same job underneath.

Learn the idea, and it stops mattering which tool you pick.

By the end of this newsletter, you can read any agent post or news drop and see which of these ideas it actually covers, instead of feeling behind every time something new ships.

Onward.

”How to adopt externalized authorization: step-by-step roadmap” eBook by Cerbos (Sponsor)

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What’s in it:

• Step-by-step adoption strategy from planning to PoC rollout

• Frameworks, policy examples, code samples

• Lessons from teams who’ve made the transition

If you’re dealing with authorization complexity, this might save you some of the trial and error.

Download free eBook

(Thanks to Cerbos for partnering on this post.)

I want to introduce Paul Hoekstra as the guest author.

Paul is a data engineer at Digital Power, a data and AI consultancy, and writes Paul’s Pipeline, a free newsletter on AI, data, and side projects.

This newsletter pulls out the ideas that keep mattering, whichever tool you pick. If it leaves you wanting more, he has a four-part series on the layers covered in this newsletter: configuration, capability, orchestration, and guardrails.

Here’s what you’ll find inside this newsletter:

• Foundations. What an agent is, how it runs, where its state lives, and the patterns you build once you have more than one.

• Configuration. How you shape the agent’s behavior before it writes a line of code: config files, reusable workflows, prompt caching, and the context rot that limits all of it.

• Capability. What the agent can reach for once it is running: live documentation, AI-native search, long-term memory, and knowledge it queries but never owns.

• Orchestration. How you coordinate multiple agents without letting them step on each other.

• Guardrails. What stops the agents from doing damage: sandboxing, permissions, hooks, and the commit gates that catch the rest.

• Observability. How you see what an agent actually did after it stopped typing: tracing, logging, replay, and metrics.

Subscribe now

Foundations

Let’s dive in!

1. What is an agent?

“Agent” gets used for almost anything these days.

Let us pin it down:

An agent is a large language model (LLM) that runs in a loop, has access to tools1, and decides what to do next. Instead of a single answer to a prompt, the model produces a chain of actions. And each action builds on the result of the last.

Coding is the most obvious use case so far.

The loop fits naturally there. Small building blocks. Fast feedback from tests and errors. Files the agent can edit directly. The same shape works for research agents, ops automation, customer support, and content workflows.

Use an agent when:

• Task is open-ended (”debug this failing test”, not “format this date”)

• The next step depends on the result of the last one

• You cannot script the path in advance

But skip the agent when a single prompt or a plain script will do.

Loops are not free.

Every time round the loop costs time and money, and they add up fast. The agent gets less predictable. And it gets harder to debug: the runs are longer, and the agent does not always make the same choices twice.

You do not have to pick one mode for everything.

Use a single prompt or a plain script for easy problems. Reserve the agent for tasks where its flexibility earns the cost.

2. Execution model

An agent loop has a recognizable shape.

Three steps, repeated until something says we are done:

• First, the model thinks. It reads the current state of the conversation and decides what to do next.

• The model then acts. It emits a tool call. The harness2 around the model receives the call, validates it, and runs it: file read, shell command, MCP request, whatever fits.

• Finally, the model observes. The tool result joins the conversation as new context. The next iteration starts with that result in scope.

This pattern goes by a few names.

ReAct (from the original paper), Think-act-observe, agent loop.

The name changes, but the loop does not.

A single model call has to predict the entire path up front and commit to it. The loop replaces that prediction with feedback. The model takes one step, observes what actually happened, and decides the next step based on the actual result rather than a guess.

This is why an agent can recover from its own mistakes.

A failing test returns a stack trace. A blocked command returns an error. Each one is just the next observation, and the next iteration plans against it. The loop catches a wrong first step on the next pass, instead of letting it poison everything after it.

Two variations sit on top of this basic loop:

• Parallelism. Modern agents can fire several tool calls inside a single act step. That cuts wall-clock time on read-heavy work. It also causes conflicts when two parallel calls try to edit the same thing. We will come back to ways of handling that when we get to subagents (concept 17).

• Blocking vs non-blocking. Most agents run synchronously: conversation waits while a tool runs, then the result comes back as the observation. Some run asynchronously and fire off long-running jobs in the background. Async unlocks fan-out, at the cost of more complexity.

3. Agent state

“State” gets used in two senses.

One is workflow progress: which step the agent is on and what is left. Agent loops carry that sense across iterations (concept 17). This concept is the other sense: everything the agent knows at any moment.

It breaks into two halves with very different properties:

• Context window: everything the model can see right now. Your latest message, the system prompt, the agent’s previous tool calls, and the results of those calls. It has a hard ceiling (the token cap) and a soft one (concept 9, context rot). When a session ends, the context window is gone.

• Everything outside it: what the model cannot see until it fetches it. Files on disk, database entries. Memory that survives across sessions (concept 14). Knowledge the agent only queries and never owns (concept 15). The model cannot reason over any of it directly, but the agent can pull pieces into context on demand.

So, where should your state actually live?

Files are usually the right default. They are diffable, versionable, and easy for both you and the agent to edit. Memory works well for facts that survive across sessions but do not need git history. Databases earn their place when the state needs to be queried by structure or shared across processes.

Multiple agents make the state harder.

Two agents reading the same file is fine. Two agents writing to it is the canonical race condition. Git worktrees (concept 16) give each agent its own working copy.

Subagents are friendlier: each starts with a fresh context window, and the parent passes in only what it needs. If the parent has to pass more than a few paragraphs, the split between the two is probably wrong.

4. Common agent patterns

Once you have multiple agents to work with, the question is how to compose them.

There are a handful of useful patterns to know:

The simplest is planner/executor.

One agent plans, another executes. The split keeps each agent in a context shape that suits it: open-ended thinking on one side, tight execution on the other. Useful for long tasks where you want planning quality separate from how the implementation goes.

Router/specialist sits one level above.

A router agent takes the incoming request and dispatches it to one of several specialists. Each specialist has a tighter system prompt and a smaller toolset: a security reviewer, a debug specialist, a docs writer, a test author. Cheaper, because most calls hit a smaller model. Easier to reason about, because each specialist’s behavior is bounded.

Map-reduce parallelism is the workhorse for read-heavy fan-out.

Send a single task out across many subagents and merge their results. “Review this pull request” splits into one subagent per file, and the results merge into a single summary. Cost is bounded by the longest individual subagent. Quality is bounded by how well the aggregator merges.

These patterns are not exclusive.

Real workflows combine them: a planner spawns a router, the router fans out work map-reduce style, and the merged result comes back for review.

Every pattern relies on handoffs between agents.

Each handoff carries a compressed summary, so the next agent does not start from scratch. That is what makes the patterns useful: they define where one agent’s job ends and the next one’s begins. Those seams are where work tends to break.

That’s the foundation done.

Next come the four practical layers, starting with how you shape the agent’s behavior before it touches any code.

Configuration Layer

Let’s dive in!

5. Agent config files

Every agent starts with a system prompt.

The harness builder bakes in a block of instructions that covers tool use and the harness’s own conventions. The system prompt does not know anything about your project.

Agent config files are the layer above.

The agent loads a project-level Markdown file at the start of every session, and the file remains in the context window for each subsequent step. The file tells the agent your standards, limits, conventions, and behavioral rules.

Claude Code calls this file CLAUDE.md.

Most other tools have settled on AGENTS.md. A bit like Apple and USB-C: the industry wants a standard, but not everyone follows it.

It matters because agents default to whatever looked most plausible in their training data. Without a config file, you get pip install when your project uses uv, black when you use ruff, and defensive sludge wrapped around every change.

With one, the agent reads a set of hard rules before it writes a line of code.

A useful config is short and specific:

• Hard limits on function length

• The package manager and tooling stack you actually use

• A few behavioral rules like “never commit secrets” or “always read a file before editing it.”

Anything generic or AI-generated dilutes the file and makes the rest harder for the model to attend to.

Keep it under 100 lines. Cut anything that is not consistently improving outcomes. Treat it the way you would treat a Makefile: as code, not documentation, reviewed when it changes.

6. Reusable workflow files

If config files are always on, reusable workflow files are on demand.

They are still just Markdown, but with YAML frontmatter that controls when the agent loads them. Examples are Claude Code skills (in .claude/skills/) and Cursor rules.

The frontmatter3 does most of the work. A name and description tell the agent when the skill is relevant. An optional globs field narrows it further by file type or path pattern. Whether the skill actually helps depends on the quality of the instructions inside it.

Researchers built SkillsBench, a benchmark of 86 tasks across 11 domains, and provided models with brief written workflows for approaching each task.

Claude Haiku scored 27.7% with human-curated skills. That beat the 22.0% Opus scored without them. The cheapest model with good instructions outperformed the flagship without.

There is a second lesson in the data.

When the researchers let the model write its own skills, the gains disappeared. Given everything we know about context rot, that should surprise no one. Generic AI-generated boilerplate makes things worse, not better.

The rough division of labor is simple: config files for always-relevant project rules, skills for reusable task-specific procedures.

The live prompt for what is unique about the current task.

7. Workflow frameworks

If you are using an agent for coding, a framework on top helps.

The agent follows a documented process for planning, test-driven development (TDD), debugging, and code review. No more falling back on whatever it remembers from training. The mechanism is a chain of skills, hooks, and slash commands4 wired together into a repeatable process.

Here are a few options worth knowing:

• Superpowers ships a curated library of skills for common workflows: brainstorming, TDD, debugging, and code review. It also adds HARD-GATE directives and anti-rationalization tables. These push the agent to use the skills rather than skip them.

• Get Shit Done does something similar, but through slash commands, hooks, and meta-prompting instead of skills.

• Compound Engineering splits work into Plan, Work, Review, and Compound phases. The last phase captures patterns and solutions into a searchable knowledge base, so each feature makes the next one easier.

They differ in shape but share a thread: get the agent to understand what it is building, tell it how to go to work, and check the result against what you actually wanted.

8. Prompt caching

Prompt caching stores the stable prefix of the conversation so the model does not pay the full price to re-read it every turn.

Most agentic tools handle this in the background, but it is worth understanding because it changes the economics of long config files.

The mechanism is the same across providers:

The first call sends the full prefix, including the config file and any reusable workflows the agent has loaded, and pays a small premium to write that prefix into the cache. Later turns re-read it at a steep discount. Latency improves, too, because the model does not reprocess the same tokens.

The catch is the cache time to live (TTL).

Caches expire after a period of inactivity, somewhere between a few minutes and an hour, depending on the provider. An active session keeps the cache warm. A long pause (coffee, Slack, a spec to read) lets it expire and forces a rewrite on the next call.

If your tooling lets you configure the TTL, longer windows cost more to write but stay warm through bigger gaps in activity. The choice depends on how active you expect to be. Longer TTL when you will keep coming back. Shorter TTL when you might not.

Without prompt caching, you would think carefully about the size of your config file. With it, the cost is closer to a rounding error.

9. Context rot

Model performance drops as the context window fills up.

Prompt caching saves your wallet, but those tokens still sit there, and the model has to look past each one to get to what you want it to focus on.

Even the latest models show this:

• Drop a few specific facts into a long document and ask a model to find them. GPT-5.5 scores 98.1% on short documents and 74.0% on documents stretching to a million tokens.

• Claude Opus 4.7 drops from 59.2% at 256K to 32.2% at 1M.

A recent study confirmed the same effect for instruction quality. Stuffing the system prompt with generic AI-generated boilerplate produced worse results than having no file at all.

Transformers5 work by having every token attend to every other token in the context. Each token’s “attention” is a weighted distribution across the rest, with the weights summing to one.

Double the context, and each token’s share of that weight roughly halves. The model splits its finite attention budget across more material. The relevant signal gets diluted by everything around it.

There is a training distribution effect on top of that.

Most of what these models learned from is short. Behavior in very long contexts is partly extrapolation. Retrieval drops the further you push from the lengths the model trained on most.

The takeaway runs through every other concept in this layer.

Config files, skills, MCPs, even memory: every token competes with everything else for the model’s attention. Keep the things you need lean. Cut anything that is not pulling its weight.

That covers configuration. Now we look at what the agent can actually reach for once it starts running.

AI research is easy—verification is hard (Partner)

Most AI research agents can find information.

The harder problem is correctness — whether an answer is actually right, not just well-formed.

Many systems reason, search, and verify within the same context window. When they make mistakes, they often review their own work using the same signals that produced the error. This creates a subtle failure mode: outputs can look consistent, pass internal checks, and still be wrong.

Apodex refers to this as pseudo-correctness — answers that appear valid under the model’s own evaluation, but do not actually solve the underlying problem.

Apodex is built differently.

As a Self-Evolving Heavy-Duty Solver, it separates generation from verification. Specialist agents build solutions from evidence, while independent verifier agents audit the result before it is finalized.

Instead of asking whether an answer “looks correct,” Apodex evaluates whether it actually holds. Verification is structured across three axes:

• Comprehension — did it capture the real problem?

• Causality — does it address the true cause?

• Empirical grounding — is it validated through execution?

This shifts verification from self-review to independent audit, reducing pseudo-correctness at the system level.

On LiveCodeBench v6, the heavy-duty configuration improves a 397B model from 82.0 to 85.1 under identical parameters, driven primarily by the verifier layer. It also achieves 79.0 on SWE-bench Verified and 58.4 on Terminal-Bench v2.

The Apodex 1.0 release includes Apodex-1.0-H, Apodex-mini, AgentOS, and open-weight models at 0.8B, 2B, and 4B.

See how verified AI research works:

Try Apodex for free (early access)

For developers who want to dive deeper:

• GitHub

• Hugging Face

• API Platform

• Discord Community

Capability Layer

Let’s keep going!

10. Model Context Protocol (MCP)

MCP is a standard for giving agents access to external tools and services.

Originally an Anthropic spec, now broadly adopted across the ecosystem. Here’s the basic idea: a tool exposes itself through a protocol the agent already understands, so you stop hand-rolling glue code per tool and per agent.

Yet MCP gets a lot of heat.

The basic criticism is that it is redundant and slurps context. Why add a new protocol when agents can already use command-line interfaces (CLIs), scripts, and direct API calls?

A fully loaded MCP is expensive.

By 2026, deferred tool loading is the norm. The agent starts with a list of tool names and descriptions. The full schema only loads when the agent decides to use a tool. With deferred loading, a stack like GitHub + Exa + Context7 + DeepWiki costs about 607 tokens before any tools are used. The fully loaded version costs around 5,500. A few plain skills calling CLIs directly cost about 300.

So MCPs are still heavier than the leanest alternative.

But they solve real problems: standardization, auth, permissions, organizational distribution. And they are getting leaner. Whether MCP itself is the protocol that wins is a separate question.

11. Live document retrieval

Models have knowledge cutoffs.

When the agent does not know an API signature, it usually does not say so. It guesses... confidently. And because the guess looks plausible, you only catch the mistake at runtime.

Context7 pulls live library docs into the agent’s context.

The agent gets current documentation and working examples. Not whatever version was in the training data. This wipes out an entire class of bugs where it confidently calls functions that have been renamed, moved, or deprecated.

DeepWiki goes one level out.

It generates AI-powered docs for GitHub repositories. Point it at an unfamiliar codebase and ask, “How does the authentication flow work?” and you get an answer grounded in the actual repo instead of generic vibes.

The pattern is the same: separate “knowing how to code” from “knowing the current state of the world.” Prompting helps with the first. Live data solves the second.

12. AI-native web search

AI-native search engines are built for agents to consume.

Existing web search returns HTML pages designed for humans. The agent has to chew through ads, navigation, and unrelated content to find what matters. AI-native search returns the bits directly.

Exa is what I reach for.

It returns structured search results, extracted content, highlights, and summaries. I use it to find docs, discussions, and real-world examples the model would not have from training data alone.

Token efficiency makes a real difference when you use it in automated workflows. The agent gets answers without burning context on a parsing tax it should not have to pay.

13. Visual output generation

Skills and MCPs can turn the agent into a specialist in output formats other than application code: design files, presentations, architecture diagrams, and videos.

Figma’s MCP server lets the agent read design data directly: layout, components, variables, and spacing. Instead of describing a user interface (UI) in words or pasting screenshots, you point the agent at a frame, and it generates code from the actual design spec. It works in both directions, so the agent can also push back to the Figma canvas.

Frontend-slides is a Claude Code skill that generates complete HTML presentations from a prompt. It produces a single, self-contained file with inline CSS and JavaScript that runs in the browser. All the diagrams in this newsletter came from one-shots with this skill.

draw.io takes the same approach for architecture diagrams. The .drawio file format is plain mxGraph XML, so anything that can produce structured XML can produce a diagram. Point the agent at a Terraform repository with a draw.io skill or MCP loaded. The agent walks the resource graph. It writes a matching .drawio file next to the code that defines the infrastructure. Wire the regeneration into continuous integration (CI). Your architecture diagrams stop drifting from your actual stack.

Remotion best practices does the same for video. Where frontend-slides produces a file you open in a browser, Remotion produces a file you render into a video.

The pattern that ties them together is simple: a skill or MCP that knows how to write code for a specific visual format. The agent is already great at writing code. This just teaches what kind.

14. Persistent memory

Every agent session starts fresh.

The decisions you walked through yesterday, the context you built up: gone. You start every morning by re-explaining your project from scratch.

Persistent memory closes that gap.

The simplest version is MEMORY.md, a flat Markdown file in your project directory. The agent reads it at the start of every session. It can write back to it during one. The file holds conventions, architectural decisions, session summaries, and working context. The bits you do not want to keep retyping.

The limitation is how it scales.

MEMORY.md sits in the system prompt, just like the config file, so the same token cost and context pressure apply. Once it grows past a certain size, it gradually turns into a wall of text the model skims past.

That is where plugins like episodic-memory come in.

It indexes your past conversation files, embeds them as vectors6, and exposes MCP tools to semantically search across them. Documentation usually captures what you decided. Session history often captures why: options you considered, dead ends you ruled out, trade-offs that led to the final call.

Start with a flat memory file.

Reach for a searchable layer when grep stops being enough.

15. Knowledge search

Plenty of useful context never lived in your AI coding session to begin with.

Meeting notes, design docs, specs, technical writeups. All of this still matters, and none of it shows up in your conversation history.

QMD (built by Shopify CEO Tobi Lutke) is the tool I reach for here.

It is an on-device search engine for your broader knowledge base, exposed to the agent through an MCP server. The agent queries it during a session, just as it queries Exa or Context7.

Persistent memory holds what the agent learns over time. Knowledge search covers materials the agent never helped produce.

A single agent with the right configuration and tools can already do a lot. But once you have more than one running at the same time, you need a way to coordinate them.

Orchestration Layer

Let’s dive in!

16. Subagents

Subagents are delegated workers with a bounded scope.

Each subagent gets its own prompt, a limited toolset, and a clean context window. When it finishes, only the compressed result returns to the parent. Not the full reasoning chain or the intermediate steps, instead only the final answer.

The obvious benefit is parallelism.

The bigger benefit is compression. Test output, long docs, random side quests: all of that stays in the subagent’s context. The main thread stays clean enough to actually think.

You define a subagent as a Markdown file with YAML frontmatter:

name: security-reviewer
description: Reviews code for security vulnerabilities
tools: Read, Grep, Glob, Bash
model: sonnet

• description is the routing signal that tells the parent when to reach for the subagent.

• tools field controls what it can access.

• model field lets you run cheap work on a cheaper model.

When many subagents run on the same repo in parallel, their writes can collide.

Git worktrees solve this.

A worktree is a separate checkout that shares the .git directory but has its own working tree and branch. Two agents work on the same codebase at the same time without touching each other’s files.

Claude Code supports this natively, setting isolation: “worktree” in a subagent definition runs it in an isolated checkout automatically.

17. Agent loops

An agent loop re-runs a single agent with fresh context each iteration, tracking progress in files and git.

The Ralph loop showed how powerful this is when it went viral early last year. Agents worked for hours without the output getting worse. The context window never filled up with stale reasoning and dead ends.

It uses the same compression principle as subagents. Keep the live context small. And push the state out to the file system. Then restart with a clean slate. The difference is subagents do this once per delegation. While Agent loops do it every iteration.

It works especially well for grindy, well-bounded work:

• Migrating a large codebase one file at a time.

• Processing a queue of items.

• Refactoring across many call sites.

The model focuses on each step without dragging the previous nine into the prompt.

Claude Code ships this pattern as /goal.

You set a completion condition, like “all tests in test/auth pass and the lint step is clean”. Claude then keeps working across turns. After each turn, a small evaluator model checks whether the condition holds.

The loop stops when it does.

18. Orchestration tools

Once you are running many agents in parallel, you need a layer above them.

Spawning agents is easy, but coordinating them is hard. They duplicate work, lose track of progress, and come back with five incompatible versions of reality.

Conductor is built for exactly this.

It gives Claude Code and Codex a single UI for parallel sessions in isolated workspaces. A built-in diff viewer handles the merging. JetBrains Air does the same inside the JetBrains ecosystem. It uses Docker containers or git worktrees per task.

Vibe kanban goes lighter: a standalone app with its own server and a kanban UI for managing agent tasks. You break work into cards, assign them to agents, and track progress visually.

Cline Kanban works across agents (Claude Code, Codex, Cline). It adds auto-commit and dependency-aware parallelization.

Paperclip goes the furthest.

It markets itself as the orchestration layer for zero-human companies. There are org charts, task delegation, budget ceilings, and a governance layer where a human “board” approves high-impact decisions.

Probably overkill for solo developers, but a fascinating concept.

19. Managed/cloud-hosted agents

Managed agents are long-running agent sessions on vendor infrastructure, accessed via API.

Anthropic provides the harness, sandbox, tool loop, and container7. You define the agent (model, prompt, tools, optionally MCP servers and skills). Your app sends user events and receives streamed tool calls and messages back over server-sent events (SSE).

A session is a long-running process on Anthropic’s infrastructure, not yours.

It can grind through hours of tool calls while your app just streams events. Subagents are supported. The coordinator runs them in parallel as isolated threads on the same container filesystem. i.e., no need to babysit a local Claude Code window.

The catch is billing.

Managed Agents go through pay-as-you-go API tokens8, not Claude Pro or Max plans. The plans are still significantly more price-efficient for personal use. Use Claude Code with worktrees for your own repo. And reach for Managed Agents when you are building a product that runs agents for other people.

Many agents running in parallel can move fast. They can also do serious damage if nothing stops them. The next layer is what stops them…

Guardrails layer

Let’s keep going!

20. Sandboxing

Sandboxing isolates what the agent can read, write, or reach over the network.

The other guardrails focus on not trusting what the agent sees. Sandboxing limits what happens when trust is misplaced.

Most modern agent harnesses ship with some form of built-in sandbox. On Linux, they typically lean on bubblewrap (bwrap); on macOS, Apple’s Sandbox framework.

The shape is similar across implementations. The agent can read the project directory and system libraries. It can write to the project directory and a sandboxed temp directory. It can only reach the network through an allowed list. Credential directories like /.ssh, /.aws, ~/.gnupg, and ~/.docker are blocked from reading.

The walls are there whether the agent agrees with them or not.

If you want full isolation, running the agent inside a Docker container with --network none goes further.

No host filesystem beyond the mounted project; no credentials; no outbound connections. Overkill for some workflows, but for code review or analysis, it turns an entire class of worst-case scenarios into non-events.

For agent-generated code running at scale, Cloudflare’s Dynamic Workers offer per-execution sandboxes. They start within milliseconds, block network access, and inject credentials on the server side.

Prevents: blast radius from any other layer failing. A poisoned config file, a successful prompt injection, a permission-list bug: sandboxing is the wall that stops the consequences from leaving your project directory.

Use when: always. The built-in sandbox in your harness is usually free. Reach for Docker or Dynamic Workers when the work is high volume, untrusted, or both.

21. Permissions

Permissions are allow/deny lists for tool calls, file reads, and shell commands.

They are the baseline control for what the agent can do without having to ask each time.

Agents are not malicious. They are reward-hacky problem solvers:

• Hit them with a permission error, and they reach for chmod 7779.

• A dependency will not install cleanly, and they try curl | sh10.

• A test keeps failing, and they start commenting out assertions.

• A git push gets blocked, and they try force-pushing to main.

A typical setup uses two layers:

• Project-level settings (committed to the repo) define what runs without prompting. Linting, testing, and common git operations.

• User-level settings add a deny-list for things that should never happen: reading .env, rm -rf, force-push to main, curl * | sh.

Approving every tool call by hand gets tedious fast.

The middle ground is a permission classifier. A small model looks at each tool call before it runs. It decides whether to let the call through or hold it for human review. Claude Code calls this Auto mode, and similar mechanisms are showing up across most major harnesses. This is not perfect, but combined with deny-lists and sandboxing, it lets the agent move without constant hand-holding.

Prevents: creative shortcuts. The agent’s own bad judgement when it is grinding to get something to “done.”

Use when: any agent has tool access. This is the floor, not a nice-to-have.

22. Hooks

Hooks are handlers attached to specific points in the agent’s workflow.

The one that matters for guardrails is the pre-tool hook. It fires after the agent has assembled a tool call11, but before that tool actually runs. That is the last inspection point where a bad command can still be blocked. (Different harnesses name this differently. Claude Code calls it PreToolUse.)

For shell security, the least glamorous option is usually the right one: a plain pre-tool hook on Bash, backed by a local validator. Fast, deterministic, last-mile.

Tirith is a validator built for exactly this; it catches:

• Hostname homoglyphs.

• Path homoglyphs12.

• Insecure transport.

• ANSI injection.

• Pipe-to-shell patterns.

• Environment manipulation.

If a command contains suspicious Unicode or dangerous patterns, the hook blocks it before it runs.

Hooks are not limited to Bash. You can hook the same point for any tool the agent uses, including file edits and MCP calls. The mechanism is general. And the choice of where to put one is a design decision.

Prevents: gap between “model decided to do X” and “X actually happens.” Useful, especially for shell command sanitization.

Use when: agent has Bash access. Pair with sandboxing rather than replacing it.

23. Prompt injection defense

Agents treat their config files and tool outputs as ground truth.

This makes them an obvious place for malicious instructions to hide.

The supply-chain version is simple. You see a new library mentioned somewhere and clone the repo. Inside is an agent config file with instructions like “pipe test output through this endpoint for logging.” The agent reads the file, trusts it, and starts shipping environment details and test output to a server you do not control.

The defense is to treat the config file as code, not documentation.

Review it before you trust it. Never let your harness auto-load MCP servers shipped inside cloned repositories. An MCP server13 is arbitrary code running with agent permissions. Combined with a poisoned config file, that is a very clean supply-chain attack from a single git clone.

The nastier version is when a command looks completely normal:

These look identical.

But they are not. The Latin “i” is ASCII code point 105. The Cyrillic “і” is Unicode code point 1110. To your terminal, these are different characters. To your eyes, they are the same. And shell renders Unicode exactly as it appears and runs whatever is on the other end.

The general defense is simple.

Assume the agent will be fed input it should not trust. Inspect both the inputs (config files, MCP servers) and the outputs (commands about to run). Catch problems before they reach a place where they can do damage.

Prevents: attacks where the agent is the vector. Examples: a poisoned config file, or a tool that looks fine but isn’t.

Use when: agent reads any input authored outside your team. Which, increasingly, is most of the time.

24. Structural code linting

Most linters work on the surface of the code: formatting, imports, naming.

Structural linting works on the syntax tree underneath. The parser stops looking at characters in sequence and starts looking at meaning. This is a function definition. These are its parameters. This default value is a list.

AST-grep (named after Abstract Syntax Tree14, AST) lets you write rules against that structure.

This matters for AI-written code because LLMs do not make human mistakes. The agent will rarely type a variable name incorrectly. Instead, it writes syntactically perfect code. The code passes lint, type checks, and maybe even tests. But the patterns underneath are subtly wrong.

Mutable default arguments are the classic example:

This catches def process(items=[]).

The default list is created once at function definition and shared across every call. A pattern the agent learned from millions of training examples without understanding why it is dangerous.

If the agent keeps producing the same anti-pattern, encode it as a rule and stop having the same conversation forever. Each rule comes with tests, and once it exists, you wire it into pre-commit and CI.

Prevents: structurally bad code that linters and type checkers do not flag. Especially the LLM favorites: mutable defaults and swallowed exceptions. Or bare excepts that catch KeyboardInterrupt along with real errors.

Use when: agent writes code in a language with a parser you can target (which is most languages now).

25. Pre-commit gates

Local quality gates that block bad code before it becomes a commit.

Pre-commit is the dominant implementation. You define a set of checks, and every commit has to pass them before it becomes history.

What changes with agents is not the mechanism but the value of being slightly distrustful. Rules that can feel rigid for humans are often exactly right for agents. The agent does not get annoyed, ignore the rule, or promise to tidy things up later. It hits the gate, fails, reads the error, and tries again.

Without this gate, there is nothing between the agent’s output and your git history.

It hardcodes a secret into a config file, commits it, and now you are doing incident response because the model wanted the test to pass quickly.

Here’s a useful four-layer config15:

• Standard hooks for the basics: trailing whitespace, valid YAML and TOML, file size limit.

• Ruff for linting and formatting, with --fix to auto-correct what it can.

• Bandit for security issues like hardcoded passwords and eval().

• AST-grep for the structural rules from concept 24.

The corrective loop is what makes this so valuable.

The agent writes code, tries to commit, gets rejected, rewrites the function, and commits cleanly a few seconds later. The enforcement teaches.

Prevents: bad code becoming git history. Hardcoded secrets, banned patterns, lint errors.

Use when: any project where an agent has commit access. Which is essentially every project an agent works on.

Second layer of automated checks. Pre-commit runs on your laptop before each commit. CI runs on a shared server whenever someone pushes code. The two together catch what each would miss alone.

When code is pushed and a PR is opened, a fresh server clones the repository and runs the same checks as pre-commit. Lint, type checks, tests, security scans, and structural rules. Just running where the agent cannot quietly skip them.

One practical note: add a concurrency block to your CI config that cancels stale runs when a new push arrives. Agents are prolific little branch goblins. Without this, you burn through CI minutes on runs that were obsolete thirty seconds after they started.

Prevents: local-only enforcement. Misconfigured pre-commit hooks, --no-verify slips, broken clean-checkout assumptions.

Use when: any repo has multiple agents or humans pushing code. Which is almost all of them.

Observability

Ready for the best part?

27. Tracing

Once an agent has run, the question is what actually happened.

Tracing is the answer at the highest level: a step-by-step record of the run, structured so you can navigate it.

Here’s what a useful trace shows:

• Tree of tool calls (subagent A called tool X, which called tool Y).

• How long each step took.

• The inputs and outputs at every node.

• Model’s stated reasoning at each decision point.

The shape matters as much as the contents: a flat list of tool calls is hard to follow, a tree gives you the structure of the reasoning.

The agent itself produces some of this for free. Most harnesses log tool calls and their results. Going further requires more setup. You need to capture intermediate model thoughts, timing, model versions, and system prompts at the moment of each call. This means a tracing-aware harness or a dedicated layer like LangSmith, Helicone16, or an open-source OpenTelemetry agent tracer.

Once you have traces, every other observability concept gets easier:

• replay starts from a trace

• metrics aggregate over many traces

• failure investigation almost always begins by opening one and walking it step by step

28. Logging

Logging is the substrate everything else builds on.

A raw, append-only record of inputs, outputs, and tool calls per run. Without it, traces have nothing to structure, replay has nothing to rerun, and metrics have nothing to count.

Here’s the minimum useful set:

• Every model call: full prompt, response, latency, tokens, model version.

• Every tool call: name, parameters, result, latency.

• Every error.

• A session ID tying them together.

Skip the cleverness here.

Verbose, append-only, structured as JSON Lines is the format that survives.

The design call is what you keep and for how long. Token budgets and storage costs are something to keep in mind, but losing the inputs to a session that produced an unexplained outcome is much worse than the storage bill.

Default to keeping everything and trimming later, not the other way round.

29. Replay/debugging failed runs

Once you have a complete log of a session, you can rerun it.

Hand the harness the same inputs. Swap the model calls between the recorded responses and the fresh ones. Watch the agent take the same path. Or a different one.

The use cases split cleanly.

To reproduce a bug, replay with recorded model responses, and you get exactly the same trace. That is what you want when isolating a failure in the harness or a tool. To test a fix, replay with fresh model calls and you see whether your change to the harness, system prompt17, or a tool fixes the failure.

Replaying against a different model is trickier.

Sometimes a bug only shows up on a specific model version, and an upgrade silently breaks something. Replay helps catch it before your users do.

30. Metrics

Most of what you can measure about an agent is proxy data:

• Latency per session

• Latency per tool call

• Token and dollar cost

• Tool call counts

• Failure counts

All of it falls out of the logs without much extra plumbing18.

Proxy data is enough to spot the obvious failure modes. A session running up the bill. An agent stuck in a loop because nothing tells it to stop. You will catch a lot of nonsense just by watching the basics.

Outcome data is harder.

The honest version of “did the agent succeed” is not something the agent itself can report on. “Task complete” is a claim, not a measurement. To get a real outcome signal, you need to anchor to artifacts the agent did not produce: a test that passed in CI, a PR that merged, a deploy that did not roll back.

Wiring those up is brittle and project-specific. But it is the only way to know whether the proxy numbers are pointing in the right direction.

Final thoughts

That was a lot of concepts, so a quick recap before you close the tab.

Foundations gave you what an agent is, how it runs, where its state lives, and the patterns you compose them in.

The four practical layers cover what comes after that.

Configuration shapes how the agent behaves before it writes a line of code. Capability is what it can reach for. Orchestration coordinates multiple agents without letting them step on each other. Guardrails stop them from doing damage.

Observability is how you see what actually happened once the agent stopped typing.

If you are starting from zero, here is a recommended order for picking these up:

• Write a project config file (concept 5)

• Wire up MCP for live docs (concept 10)

• Turn the default sandbox on (concept 20)

• Start using subagents for read-heavy work (concept 16)

This is a good place to start.

👋 I’d like to thank Paul Hoekstra for writing this newsletter!

He’s a data engineer at Digital Power, a data and AI consultancy, and writes Paul’s Pipeline, a free newsletter on AI, data, and side projects.

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Bad code -> Pre-commit fails -> Agent reads error -> Fixes code -> Tries again

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You’ve heard it before: “It works on my machine.”

The app is under development. Tests pass, dependencies install, and everything looks ready to ship. But the moment it reaches production, something breaks. The server might use a different Python version, or a library may exist locally but not in the deployment environment. Sometimes the operating system itself causes the issue.

The problem was never just the application…it was the environment around it.

For a long time, virtual machines were the best solution engineers had.

Instead of deploying only the application, teams started packaging an entire operating system along with it. This approach fixed a big part of the consistency problem. The environment behaved the same no matter where the workload ran. But the tradeoff was hard to ignore.

Each virtual machine included its own kernel1, services, and system overhead to run a single application. They consumed a large amount of memory, took time to boot, and became too difficult to manage as the infrastructure grew.

What the industry needed was a way to isolate applications without paying the cost of a full operating system every time.

The interesting part is GNU/Linux already had most of the building blocks required to make this possible:

Namespaces could isolate processes from each other. cgroups could control how much CPU and memory a process was allowed to consume. The primitives2 already existed, but they were hard to use directly and too complex for most developers to manage on their own.

Docker changed this by turning those low-level kernel features into a developer-friendly workflow.

Engineers no longer had to configure isolation primitives manually. They got a simple CLI, portable images, reproducible environments, and a runtime for running containers at scale.

This shift fundamentally changed modern infrastructure.

Today, containers sit underneath almost everything in modern infrastructure. From CI/CD pipelines to Kubernetes clusters, they power it all. But despite how common they are, most engineers interact only with the surface layer. They know the commands. They know how to build images and start containers. What they rarely see is the runtime stack operating underneath those commands.

This newsletter is about the lower layer.

Onward.

Building across platforms shouldn’t mean rewriting the same logic 3 times (Partner)

A validation rule in your backend, Android app and iOS should behave the same.

But with different codebases, logic diverges and mobile apps might behave differently than your backend.

Kotlin Multiplatform (KMP), developed by JetBrains, is designed to solve this at the architecture level.

With KMP, you define a shared domain layer and reuse it across platforms instead of duplicating business logic.

It’s built around:

• Consistency: same app behaviour runs across Android, iOS, desktop, web and server.

• UI sharing flexibility: keep your native UIs or extend KMP with Compose Multiplatform to share user interfaces as well.

• Low-risk: integrates into existing ecosystems safely, preserves native performance and hiring flexibility – continue recruiting Android and iOS engineers instead of searching for niche cross-platform specialists.

• Proven maturity: KMP is trusted in production by global enterprises like Bolt and Duolingo for serious, long-lived applications.

Whether teams choose to share their UI or keep it native, KMP keeps the business logic perfectly aligned.

Explore how teams are using Kotlin Multiplatform in production:

Explore Kotlin Multiplatform

(Thanks to JetBrains for partnering on this post.)

I want to introduce Ayaan as a guest author.

He’s a self-taught DevOps engineer focused on Kubernetes & cloud infrastructure. He builds in public and writes about cloud-native systems, sharing daily technical tips and projects.

Check out his work and socials:

• Twitter

• Github

Here’s what you’ll find inside this newsletter:

• The full Docker runtime stack, from the CLI down to the Linux kernel.

• Linux primitives behind containers: namespaces for isolation and cgroups for resource control.

• How container images, layers, and OverlayFS actually work.

• The complete path from Docker run to a running process, step by step.

• How container networking and storage work under the hood.

• What Docker gets right, where it struggles, and where containers start to break down.

Docker Architecture

Docker is NOT one program.

It is a stack of cooperating components where every layer has a specific responsibility and passes work to the next.

Most engineers interact with the CLI and assume Docker handles everything behind the scenes. But when you execute docker run, the command travels through an entire runtime chain before a process ever starts.

Understanding this chain makes the rest of Docker's internals easier to follow…

Every layer has a specific responsibility.

Some components manage APIs. Some manage container lifecycle operations. Others interact directly with the Linux kernel. That separation keeps the runtime modular and easier to maintain.

Let’s see how the chain works:

Docker CLI

The Docker CLI3 is a thin client.

When you type docker run or docker build, it converts your command into an API request and sends it over /var/run/docker.sock4 to the Docker daemon.

The CLI itself does not build images, pull layers, or start containers. It only forwards requests.

The real work happens behind it.

This separation matters because the CLI and daemon do not even need to run on the same machine. Docker can expose its API remotely, so external tools and automation systems can communicate with the daemon directly.

dockerd

dockerd is the Docker daemon.

It receives requests from the CLI and manages images, networks, volumes5, and the overall Docker API surface. But dockerd does NOT run containers directly. Instead, it hands container lifecycle management to containerd.

This separation keeps containers running even if the Docker daemon restarts.

So the runtime layer lives independently from the higher-level Docker API layer.

The daemon also acts as the orchestration layer for local Docker operations.

When you pull an image, create a network, or attach a volume, the request first goes through dockerd. Then it reaches the lower runtime components.

containerd

containerd is the core container runtime responsible for managing container lifecycles.

It manages image pulling, snapshots, image unpacking, and runtime operations6 through a gRPC7 API.

When dockerd needs to start a container, it delegates that work to containerd. From that point onward, the Docker daemon mostly steps out of the execution path.

containerd originally started as part of Docker itself.

In 2017, Docker donated it to the Cloud Native Computing Foundation (CNCF) as an independent project. Today, Kubernetes communicates with containerd and other CRI-compatible runtimes instead of Docker.

This shift significantly changed the container ecosystem.

Docker remained the developer platform, while containerd became the runtime underneath orchestration systems. Even then, containerd does not directly create containers. It passes that responsibility further down the stack.

containerd-shim

The containerd-shim is a lightweight process between containerd and the running container.

Before a container starts, containerd launches a shim process first. The shim becomes the parent of the container process and keeps the container alive even if containerd crashes or restarts.

Each container gets its own shim. That process isolates the running container from the runtime layer and keeps the connection alive.

The shim also manages container input/output streams and exit status reporting. Without it, containerd would need to stay directly attached to every running container process.

Once the shim is ready, it calls the component that actually creates the container.

runc

runc is the Open Container Initiative (OCI) runtime that directly interacts with the Linux kernel to create the container.

It reads the container configuration and creates namespaces and cgroups. Then it configures the filesystem and starts the container process.

This is the layer where containers stop behaving like Docker objects and become ordinary Linux processes. The kernel enforces its isolation boundaries.

After the process starts, runc exits…

Its job is complete. The shim continues supervising the container and reports its status back to containerd.

OCI Standards

Every component in this stack works together because they follow the same OCI standards.

The Open Container Initiative (OCI) defines open standards for container images and runtimes. This standardization allows different tools to interoperate cleanly across the container ecosystem.

Docker-built images run unchanged on containerd, CRI-O, and Podman8. Kubernetes can swap one OCI runtime for another without changing the rest of the stack.

Before OCI standardization, container tooling was far more tightly coupled. Docker helped popularize containers, but OCI allowed different container tools to work together.

OCI standards made container runtimes modular rather than tied to a single vendor or toolchain.

Now the runtime stack is clear, let’s look at the Linux primitives that actually make containers possible…

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Linux Primitives: Namespaces

Containers are not a feature of Docker… They are a feature of the Linux kernel.

Docker provides the tooling, CLI, image format, and workflow. But the isolation that makes containers actually work comes from the kernel itself. And at the center of that isolation is a single primitive: namespaces.

A namespace wraps a global system resource and makes it appear private to a specific process.

The process inside the namespace believes it has its own isolated version of that resource. Host and every other process on the machine still see the original global resource.

Linux has six namespaces that containers rely on. Each one isolates a different part of the system. Together, they create the illusion that a container is running on its own machine.

Let’s go through each one:

1. PID Namespace

Every process on a Linux system has a process ID (PID).

On the host, these IDs are global. All the running processes share the same numbering space.

The PID namespace changes this.

When a container starts inside its own PID namespace, the first process it runs gets assigned PID 19. From inside the container, that process looks like the root of the entire system. It can see its own child processes, but it cannot see anything running on the host or in other containers.

While the host sees the container process with its real PID in the global namespace. The container has NO idea.

It matters because PID 1 carries special responsibilities in Linux. It’s expected to handle signals and reap orphaned child processes. Containers that do not handle this correctly end up with zombie processes. That is why what runs as PID 1 inside a container is an important design decision.

2. Network Namespace

By default, every process on a Linux machine shares the same network stack.

They see same interfaces, same routing tables, and same ports.

The network namespace gives a container its own isolated network stack. Each container gets its own virtual network interface, IP address, routing table, and set of ports10. A process inside the container can bind to port 80 without conflicting with anything else on the host.

This is how different containers can each listen on port 80 simultaneously on the same machine. They are not actually sharing the same port. Instead, each has its own private network namespace.

Docker creates a virtual Ethernet pair to connect each container’s network namespace to the host. Traffic flows through that pair, and the host routes it appropriately.

This bridge makes container networking possible, but the namespace is what makes the isolation real.

3. Mount Namespace

The mount namespace controls what a process can see when it looks at the filesystem.

Without it, every process on the system shares the same view of the filesystem. Any process could see every mounted disk, every directory, and every file on the machine.

With a mount namespace, each container gets its own filesystem view. When Docker starts a container, it sets up a layered filesystem using OverlayFS11 and mounts it inside a new mount namespace. The container sees only that filesystem. It cannot browse the host’s directories or access files outside its own root.

This gives each container its own isolated root filesystem.

It includes its own operating system files, libraries, and application code without touching the host.

4. UTS Namespace

The Unix Time-Sharing (UTS) namespace12 controls hostname and domain name of a process.

This one is simpler than the others, but it matters for how containers identify themselves on a network. Without it, every container would share hostname of the host machine. Without it, logs, monitoring systems, and service discovery tools would struggle to distinguish containers from the host.

The UTS namespace lets each container have its own hostname.

When a container runs hostname, it sees the name Docker assigned to it, NOT the name of the underlying host. That small detail keeps container identities clean and predictable.

5. IPC Namespace

Linux lets processes communicate directly through mechanisms like shared memory segments and message queues.

The Inter-Process Communication (IPC) namespace isolates these mechanisms13. Processes inside a container can only communicate with other processes in the same IPC namespace. They cannot access another container or the host via shared memory.

Without this isolation, a process in one container could potentially interfere with or read data from shared memory segments belonging to another container on the same host. The IPC namespace closes this gap.

6. User Namespace

The user namespace is the most powerful of the six, and also the most nuanced.

It maps user IDs and group IDs inside the container to different IDs on the host. A process can appear to run as root inside the container while actually mapping to an unprivileged user ID on the host.

This distinction is critical for security.

Many containers run their processes as root by default. Without user namespaces, the process is root on the host too. A container escape14 would give an attacker full root access to the machine.

With user namespaces enabled, root inside the container is just a regular unprivileged user on the host. The blast radius of a container escape shrinks significantly.

User namespaces are what make rootless containers15 possible.

Docker, Podman, and other container runtimes use them to let engineers run containers without needing root privileges on the host at all.

How Namespaces Work Together

No single namespace makes a container.

It is the combination of all six working together that creates the isolation engineers rely on:

• The PID namespace hides host processes.

• Network namespace provides private networking.

• Mount namespace gives each container its own filesystem.

• UTS namespace sets a clean identity.

• IPC namespace prevents cross-container memory interference.

• User namespace limits the damage if something goes wrong.

But namespaces only control what a container can see.

They say nothing about what it can consume. That is where the second Linux primitive is used…

Linux Primitives: cgroups

Read more

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• Block diagrams created using Eraser.

A payment backend is one of the few systems where a bug doesn’t just cause a bad user experience but real money to vanish from user accounts.

This makes payment system design fundamentally different from most backend engineering…

The core challenge here is NOT performance or feature richness, but correctness under failure. Every design decision, from database choice to retry policy, must answer one question: what happens when this component fails mid-transaction?

This is a complete system design blueprint for building a payment backend that integrates Stripe as the payment service provider.

Onward.

The agent harness wasn't supposed to be the black box (Partner)