This is the eighth article in a series on agentic engineering and AI-driven development. Read part one here, part two here, part three here, part four here, part five here, part six here, and part seven here.

“640K ought to be enough for anybody.”—Bill Gates (allegedly)

If you’re building AI agents that do complex, multistep work, you’re going to run into context loss. The agent’s working memory fills up, older information gets silently dropped or compressed, and the agent keeps going without realizing it’s forgotten something. This article, the third in my Radar article trilogy about context management, walks through a pattern I’ve been refining for detecting and recovering from that problem, which I call the externalize-recognize-rehydrate pattern (or ERR, which I think is actually a pretty good acronym for an error recovery pattern): save your agent’s state to files on disk, detect when context has degraded, and reload from those files to recover. The individual techniques are standard practice in agent and skill engineering—checkpointing, progress files, state verification—but the real power comes from combining them into a coherent workflow that you can use live or build into your agents. I’ll walk through each step with specific prompts you can adapt for your own agents and coding sessions.

Which brings me to memory. Gates has said on multiple occasions that he never actually said that quote at the top of this article, but it endures because it captures one of the core limitations of that era, one that people struggled with constantly, in a way that we can laugh about now. Around that time I was using a 286 with 1 MB of RAM. That’s megabytes, not gigabytes. MS-DOS 3.3 gave me 640K of conventional memory plus 384K of upper memory, and I spent a lot of time figuring out how to use every bit of it. I configured memory managers, loaded device drivers high, used (and wrote!) terminate-and-stay-resident programs that moved themselves out of conventional memory to free up space, and generally treated memory as a resource that required active, deliberate engineering. There was a lot I wanted to do that didn’t fit into 640K, and like most people at the time, I went to some lengths to compensate for the memory limitations.

We’re at the 640K stage of AI development. The context window is the new RAM ceiling. Most of today’s models give you somewhere between 200K and 2M tokens of working memory (and, like memory in the late 1980s and early 1990s, those numbers are growing all the time), and if you’re building agents that do complex multistep work, you will hit that ceiling. When you do, the AI starts compacting: compressing or dropping older parts of the conversation to make room. And just like running out of conventional memory on a 286, things stop working right and you’re not sure why.

In 20 years we’ll be looking back at today’s puny context windows and wondering how developers in the 2020s managed to get anything done with just a few million tokens. Because none of this is new. In case you don’t believe me, here’s a photo of my dad at Princeton in the early 1970s working on an Evans and Sutherland LDS-1 graphics computer, the first commercial vector graphics machine, connected to a PDP-10 mainframe:

The actual LDS-1 is in the large cabinet in the background, directly behind the monitor. Sitting next to it, just out of the picture, is an even larger cabinet that holds a memory unit with 16K of magnetic core memory (technically 8K words).

So you can imagine that just a decade later, 640K in a tiny PC that fit on your desktop seemed extravagant.

In the last two articles in this series (“Why Doesn’t Anyone Teach Developers About Context Management?” and “Your AI Agent Already Forgot Half of What You Told It”), I talked about what context is and why context management matters, and I shared practical techniques and prompts for keeping important information in files instead of leaving it in the AI’s context window. This article gets more technical. I want to build on those strategies and talk about how to build agents that can detect when they’ve lost context and recover from it on their own.

Brute-forcing my way through context loss

I’ve been doing this kind of context management for a while now, long before the specific tools I’m about to describe existed. But a recent crash gave me a clean example of what the process looks like in its most brute-force form.

I was working in Copilot with a seven-step plan, going through it one step at a time, having another AI review each step before moving on. Steps one and two went fine. When it came time to do step three and I gave it the prompt, it jumped straight to step four. This kind of thing can be really frustrating, because it seems like an AI smart enough to implement a complex feature in code should be able to (ahem) count to four.

The key to not getting frustrated when the AI loses track of steps or can’t seem to count from prompt to prompt is to remember what it’s good at and how it remembers things. If the AI you’re using does that, check the conversation history. You’ll probably see something like “summarizing conversation history” or “compacting conversation” somewhere above your last message. That’s telling you that the AI lost track of where it was because that count was literally purged from its memory.

AIs are good at carrying out an instruction. They’re bad at keeping track of their own state over a long conversation, and the way they manage their memory is a big part of that. This article is about finding ways to build your AI tools so you’re not relying on them to do the thing they’re worst at.

But compaction isn’t the only way your AI loses context. A few weeks ago I was deep into a long session with Copilot, working through a multiphase code review. I’d spent a while building up context with the AI about my codebase and the decisions we’d made together. I was about to move on to the next phase, and then I got this:

The entire context was wiped, which could have been a really frustrating problem, since I had a long history with the session, and it had built up a lot of knowledge about what we were doing. This turned out to be a bug in Opus 4.6’s interaction with Copilot’s conversation history, and I’ve seen other people hit the same thing. I was staring at a fresh prompt with nothing in it.

So I did something that, in retrospect, is a pretty good brute-force version of what this whole article is about. I recognized the context was gone (hard to miss when the whole conversation disappears). I copied the entire conversation out of Copilot and pasted it into a text file. Then I gave the new session a prompt:

We were in the middle of a long conversation, then I got an error and the entire context was wiped. I saved a copy of the conversation in #file:chat_history.txt, read it and bring yourself back up to speed.

And it worked! This brought the new session back to where I needed it to be.

That simple error and recovery actually outlines a pretty good pattern for dealing with context loss:

1. Externalize the state. Get the important information out of the conversation and into a file on disk, where it won’t disappear when the context window reshuffles.
2. Recognize the loss. Notice that the agent’s working context has been wiped or degraded, whether that’s obvious (like a crash) or subtle (like output that quietly stops making sense).
3. Rehydrate from the file. Point a new session at that file and let it rebuild its understanding from what’s written down.

The individual mechanics are well-documented across cognitive science (cognitive offloading, task resumption), software engineering (the Memento pattern, React hydration), and knowledge management (the SECI model). I’m not claiming to have invented any of them. But the specific abstraction of these three phases into a unified, named pattern applied to AI context management is, as far as I can tell, new. It’s synthesis and codification, not invention.

In this case I did it with copy and paste, which isn’t particularly elegant, but it worked for me. But this is a blunt instrument, because a raw conversation dump is both too much and too little: it’s too much because it’s full of noise, like tool calls, dead ends, back-and-forth that doesn’t matter anymore; and it’s too little because the context that got silently compressed away during the session is already gone. When you build these mechanisms into agents and skills, you can do it in a much more subtle and automated way.

Externalize: Add two layers of state to your agent

The idea behind externalization, or periodically saving your agent’s state, came out of a conversation I was having with an AI assistant while building the Quality Playbook, an open source AI coding skill that runs structured code reviews. The playbook runs a structured code review as a single process, but that process could easily turn into a 15-million-token request if you tried to do it all in one shot. I described in the previous article in this series how I broke it into six phases, and that was only possible because the context for each phase had already been externalized. Each phase reads its inputs from files, does its work, writes its outputs to files, and stops. The next phase picks up from the files, not from whatever the agent remembers. If this sounds like the familiar advice to ask the AI to plan before you ask it to implement, it’s the same principle applied to context management. Separating each step and persisting the output means you can inspect it, and the next step doesn’t depend on the agent’s memory.

But what should those files contain? I found that the AI is actually good at figuring that out. At some point I asked the assistant:

Would it make sense for the agent to record more context in files as it progresses, to make sure nothing is dropped along the way? It should work even if you break it into separate prompts, because the result from each step is persisted. Plus, we can audit its reasoning for debugging and improvement.

That prompt was all it took. The assistant designed the file structure itself: a progress tracker that records which phase is active and what’s been completed, a JSONL artifact file (JSONL is just a file with a bundle of JSON objects, with one record per line) where each pass appends its output, and a set of brief documents describing the purpose of each phase. You don’t need to overengineer this. Tell the agent what you’re trying to preserve and let it figure out the file layout.

What emerged falls into two categories that I think of as execution continuity and task continuity:

• Execution continuity is the state the agent needs to resume work in the middle of a task: what step it’s on, what it’s completed, what decisions it’s made so far. These files change constantly as the agent works.
  
• Task continuity is the broader context that doesn’t change during execution: what the whole task is about, what success looks like, what the structural constraints are. These files are written once and read at every resumption.

When an agent needs to resume after suspected compaction, it reads back both layers. The task continuity files anchor it back to what the whole endeavor is about. The execution continuity files put it back in the middle of the work. Together, they give the agent enough information to continue without relying on anything that might have been compacted.

The key is that externalization isn’t something you do once at the beginning of a task. You want the agent saving its state at frequent checkpoints so that if compaction happens mid-run, the most recent checkpoint is close to where the agent was working. Here’s the kind of instruction I gave the agent for tasks that processed records one at a time:

Update the progress file after every single record, not in batches. Write the output line first, then update the progress file with the new cursor and a fresh timestamp. If the progress file’s timestamp falls behind the output file’s, you’re batching and that’s wrong.

The frequency matters because context can compact at any point. If the agent only saves state at the end of a long run, compaction in the middle means losing everything since the start. If it checkpoints after every unit of work, the worst case is losing one unit.

Two-layer externalization survives context reshaping, not only outright context loss. Even if the agent’s context window isn’t full, if the context has been reorganized or reprioritized (a compression that reshapes without truncating), the agent can reload the external files and know for certain what the ground truth is.

Recognize: Detecting loss from inside the agent

The second step in the pattern is to recognize that your agent has lost context, and it turns out to be the hardest part (at least with today’s AI technology). When the context window fills up, the AI compacts silently, and the agent keeps working without realizing it’s lost information. The agent can’t tell you it’s forgotten something, because it doesn’t know it forgot. Detecting that change turns out to be a nontrivial problem; I’ll walk you through an approach that helped me, and keep it general enough so you can do the same thing. The copy-and-paste approach works when the context loss is obvious, like a crash that wipes your whole conversation. But most context loss isn’t that visible.

I described context compaction in the previous article, but it’s worth restating the core problem from the agent’s perspective. Different tools handle context overflow differently: Some truncate older messages; some compress conversations into summaries; some use a sliding window. But they all have the same effect. Information disappears from the agent’s working context, and the agent doesn’t get notified.

This was a challenge when I built the Quality Playbook, because it runs multiple passes over a codebase, each one reading source files, extracting requirements, and checking coverage. Each pass can involve enough work that it fills the context window multiple times over. And when context compacts mid-pass, the agent doesn’t know it happened. It keeps working, but the output starts silently degrading. So I started building mechanisms for the agent to detect compaction and recover by reading back the files it had written earlier. The patterns that came out of that work are general enough to apply to anyone building agents that need to survive context pressure.

From the agent’s perspective, compaction is seamless. It’s tracking state, referencing decisions made earlier in the conversation, and then at some point the earlier context is gone. But the agent can’t tell the difference between “I never knew that” and “I knew it but lost it.” It tries to reference something and finds nothing, or finds a compressed version that lost the nuance. And because the agent doesn’t know it lost anything, it doesn’t know it needs to recover.

This invisibility is the core problem. But it turns out you can work around it, and the next two sections walk through how.

Building a detection mechanism

Once you have files on disk, the question is what specifically to check and how to know when something has gone wrong. I landed on a mechanism while building the Quality Playbook’s requirement extraction pipeline. The playbook processes source documents in multiple passes, and each pass appends its output to a JSONL artifact file. After each unit of work, the agent also writes a progress record to a separate file: what it just finished, what it found, and where it should pick up next.

The detection mechanism comes from two rules I gave the agent. The idea is that the progress file tracks a cursor, which is just a position marker that tells the agent which record to process next. If the agent writes a record to the output file but then loses context before updating the progress file, those two files will be out of sync.

The agent didn’t need to understand any of that upfront; I just described the rules in plain language and let it figure out the implementation. The first rule establishes an invariant between the output file and the progress file:

Cursor advances only after the line is on disk. Write the summary line to the output file first, then update the progress file. The cursor must always equal the index of the next record that still needs to be processed.

The second rule told the agent how to check that invariant on startup:

On startup, read the progress file. Resume from its cursor value. Verify continuity: the last line in the output file should equal cursor minus one. If not, roll the cursor back to match disk state and report the discrepancy.

If the progress file says the cursor is at record 381, but the last line in the output file is record 379, something happened. The context compacted and the agent lost track of where it was. The divergence between the two files is the signal.

This worked because files on disk don’t change when context compacts. They’re written once and then read repeatedly. If what the agent thinks it knows doesn’t match what’s actually in the files, something shifted in the agent’s memory, not on disk. I ended up folding this check into a preamble that every session started with:

If this session has experienced auto-compaction, re-read the pass specification from disk. Do not try to reconstruct it from the compacted summary. Read the progress file. Read the last record of the JSONL artifact and confirm its index equals the cursor minus one. If not, roll the cursor back to match disk state. Disk is the source of truth. The conversation is not.

That preamble ran at the top of every session. During one particularly intensive day of pipeline development, I ran over a hundred Claude Code sessions with that exact instruction. Most of them completed without hitting compaction. But the ones that did hit it recovered cleanly, because the preamble told the agent exactly what to check and exactly what to do when the check failed.

The specific prompts I used are tied to the Quality Playbook’s file structure, but the technique generalizes. If you’re building any agent that does multistep work, you can adapt the same approach. Here’s a version you could drop into a session preamble or an agent’s system prompt:

Before continuing any task, read your progress file and your most recent output file. Compare them: does the progress file say you’ve completed work that isn’t reflected in the output? If so, trust the output file, roll back your progress to match, and note the discrepancy. Do not rely on what you remember from the conversation. The files on disk are the source of truth.

The wording doesn’t have to be precise. What matters is the structure: tell the agent where to look, what to compare, and which source to trust when they disagree.

But didn’t you just say the AI can’t detect its own compaction?

Right, and it can’t. What I described above isn’t the agent detecting compaction. It’s the agent running a deterministic check against files on disk and finding a discrepancy. The agent doesn’t need to know that compaction happened. It just needs to notice that two files disagree. Think of the agent as an amnesiac clerk. You don’t ask the clerk to remember what they did yesterday. You make the clerk check the physical ledger every time they sit down at the desk. If their notes disagree with the ledger, they’re trained to trust the ledger.

If you saw Christopher Nolan’s breakout movie Memento, you can think of your agent as Leonard Shelby, the character played by Guy Pearce with anterograde amnesia. You couldn’t ask Leonard to remember what he did yesterday. He had to check his tattoos every time he woke up. If his tattoos disagreed with what he’s seeing, he trusts the tattoo (which leads to a major plot point, which I won’t spoil). Again, this isn’t a new idea either. I mentioned the Memento pattern earlier, which is literally named after this movie.

This is a classic distributed systems technique. In double-entry bookkeeping, you maintain two independent records of the same transaction and reconcile them regularly. If they disagree, you investigate. You don’t need to know why they diverged; the divergence itself is the signal. A two-phase commit works the same way: write the data first, then update the record that says the data was written. If you find data without a matching record, or a record without matching data, something went wrong between the two phases.

That’s exactly what the cursor invariant does. The agent writes the output line first, then updates the progress file. If those two files are out of sync, something happened between the two writes. The agent doesn’t detect compaction. It detects a broken invariant, and it’s been told that when the invariant breaks, the files on disk win.

Three things make this work. First, the check is purely deterministic: read two files, compare two numbers, act on the result. There’s no reasoning involved, no judgment call about whether the agent “feels” like it lost context. I wrote about this principle in “Keep Deterministic Work Deterministic”; you never want an AI making decisions that a file comparison can make for it. Second, the files on disk don’t change when context compacts. They’re the stable reference point that the agent’s memory gets checked against. Third, the instruction to run the check lives in the system prompt or preamble, which is generally preserved even when conversation context gets compacted. The check survives the thing it’s designed to detect.

Rehydrate: Reading back the state

Rehydration is the process of reading back externalized state and rebuilding the agent’s working context. Once the agent detects compaction (or, more specifically and accurately, has enough evidence from the filesystem that compaction occurred), the recovery step is to read back the externalized files and rebuild. For the Quality Playbook, rehydration meant:

1. Read the phase brief to re-anchor the purpose of this pass
2. Read the progress file to know which unit is active and what’s been completed
3. Read the tail of the JSONL artifact to confirm the last successfully written record
4. Recompute the next unit of work from those files

This is different from just continuing without detection. Without detection, the agent tries to pick up where it left off and hopes it still has enough context. With detection, the agent knows something happened and deliberately reloads state before continuing.

You can make the rehydration process itself auditable. Instead of silently reading the files and resuming, have the agent write down what it learned:

Read the progress file and the JSONL artifact. Write a summary of what you learned: what pass is running, what unit is active, what the cursor position is, and how many requirements have been extracted so far. Then continue from there.

Writing a rehydration summary serves two purposes. It gives you visibility into what the agent understood and whether it rehydrated correctly. And it forces the agent to process the external files explicitly rather than just loading them into context. Explicit processing is more reliable than silent loading because the agent has to commit to an interpretation, and you can read that interpretation and catch mistakes.

You can adapt this approach to any agent workflow where work happens in steps. The specific files and cursor values are particular to my pipeline, but the underlying technique is general: have the agent write its progress to a file after each step, and check that file against its output at the start of every session. And this advice isn’t just for writing agents or skills. Even in a live session with Claude Code, Cursor, or Copilot, you can tell the agent to periodically write a summary of what it’s done and what it plans to do next to a file on disk. If the session crashes or the context gets long enough to compact, you can point a new session at that file and pick up where you left off. The key is getting the state out of the conversation and onto disk before you need it.

Context management is an architectural concern

Every technique I’ve described in these articles comes down to the same principle: Important information shouldn’t live only in the agent’s context window. The previous articles covered how to put that information on disk. This one covers how to make the agent aware of its own limitations so it can recover when context pressure gets too high.

An agent that can detect its own degradation and correct for it is fundamentally more reliable than one that just keeps going. When the agent knows how to stop, check itself against ground truth, and reload what it lost, context pressure becomes a recoverable event instead of a slow, silent failure.

This concludes my mini-series trilogy of articles about context management. The first article in this series was about understanding what context is and why it disappears. The second was about getting important information out of the conversation and onto disk before you need it. This one is about closing the loop: making the agent aware of its own limitations so it can detect degradation and recover from it. Together, they add up to treating context as an engineering problem rather than something you hope works out.

These are still early days. Context windows will get larger, compaction will get smarter, and some of the workarounds in this article will eventually be unnecessary. But the underlying principle won’t change: If your agent’s ability to do its job depends on information, that information needs to live somewhere more durable than working memory. That was true for my dad’s 32KB core memory at Princeton, it was true for my 640K of conventional RAM, and it’s true for today’s 200K-token context windows.

The Quality Playbook and Octobatch are open source projects where these techniques are used in production. Both are built using AI-driven development and available for exploration if you want to see how this looks in practice.

Disclosure: Aspects of the approach described in this article are the subject of US Provisional Patent Application No. 64/044,178, filed April 20, 2026, by the author. The open source Quality Playbook project (Apache 2.0) includes a patent grant to users of that project under the terms of the Apache 2.0 license.

The demo to production Death Valley

If you’ve worked on an AI feature, you know the feeling. You start building something that you are excited about, set launch timelines. The model spits out a perfect response, the prototype works magically, and everybody in the room is mentally calculating how big this product will be when we launch. I’ve been in that room a lot many times and it’s fun.

Then you try to test before you ship.

Latency spikes to 10 seconds on mobile. The model starts hallucinating on edge cases that happen to represent 15% of actual user queries. Your A/B test shows no statistically significant engagement lift because the variance in AI outputs makes traditional hypothesis testing basically meaningless. The safety team flags 340 failure cases in the first week, and you’re now debugging nondeterministic cases that fail in creative, novel ways every single day.

Most often than not, it’s not a model problem but an engineering discipline problem. Shipping an AI product is very different from traditional software. I’ve figured this out the hard way. This playbook shares my learnings.

Latency budgets

Every AI feature comes with a latency tax. Large language model inference takes time. We’re talking 500 milliseconds to 5 or even 50 seconds depending on model size, input length, and infrastructure setup. For consumer products where people expect sub-200-millisecond interactions, this is a hard constraint you have to design around.

The mistake I see most often is teams measuring only p50 latency. A feature with 800 milliseconds p50 sounds fine until you discover the p90 is 15 seconds. That means 10 in every 100 users sit there waiting for 15+ seconds. At scale, that’s thousands of terrible experiences per day.

The way I think about it is you define your latency budget by interaction type, not globally: Synchronous interactions, where the user is staring at a spinner, need to resolve under 1 second. Progressive interactions, where output streams token by token, need first token in under 500 milliseconds and full response under 5 seconds. Asynchronous interactions, where the user keeps doing other stuff, can take up to 20 seconds with a progress indicator.

You also need to measure cold starts separately. The first request after a model loads into memory can be 10 times slower than subsequent requests, and if your traffic is bursty, cold starts will disproportionately punish your most engaged users arriving during peak hours.

Besides, you also need to budget for the full pipeline, not just inference. A typical AI feature pipeline including input preprocessing (tokenization, context assembly, and prompt construction), model inference, output postprocessing (parsing, formatting, safety filtering, etc.), and a full response delivery adds up. Optimizing inference while ignoring the rest is like tuning your engine while driving on flat tires.

Lastly, use streaming aggressively for generative features. Pushing tokens to the user as they’re generated instead of waiting for the full response changes how users perceive latency.  A four-second response that starts appearing at 300 milliseconds feels dramatically faster than one that pops in all at once. Perception is reality when it comes to user experience.

Designing fallbacks

Traditional software fails in boring, predictable ways. AI features fail in novel, unpredictable, and occasionally creative ways. I once saw a model respond to a product recommendation query with a poem about loneliness. Your fallback strategy needs to be considerably more sophisticated than a try/catch block.

I think about fallbacks as a hierarchy. First, model fallback: When your primary model fails, drop to a simpler, faster, and more reliable model. Most failure cases get handled without the user ever knowing. Second, cache fallback: For queries similar to stuff you’ve seen before, serve a cached response. Third, template fallback: When generation fails completely, fall back to prewritten templates. Degraded beats dead every time. Fourth, graceful omission: Sometimes the best fallback is to simply not show the AI feature at all rather than showing a broken version.

The design principle underneath all of this is that users should never encounter an unhandled AI failure. Every failure mode maps to a specific level, and transitions between levels should be invisible whenever you can manage it.

Quality measurement

Quality in traditional software is binary. The button works or it doesn’t. AI feature quality is continuous and subjective, and it changes depending on context. I’ve landed on a four-layer quality pyramid.

The foundation is safety, and it’s nonnegotiable. Does the output contain harmful content, PII, or made-up facts? This layer is binary, and you measure it with automated classifiers running against 100% of outputs.

The second layer is factual correctness, which is domain specific. Is the output actually right? For a coding assistant that means generated code compiles and passes tests. For a writing tool it means grammatical, stylistically appropriate output. You measure this with domain specific evaluation suites.

The third layer is usefulness, and it’s user centered. Did the person actually benefit? Track acceptance rate, edit distance, time to task completion, and repeat usage. This is where traditional product metrics meet AI specific ones.

The fourth layer is delight, which is experimental. Does the output feel good? Hardest to measure but often most important for adoption. Sometimes the numbers say the feature works but users’ guts say it doesn’t. This layer catches that gap.

A/B testing AI features

A/B testing AI features is fundamentally harder than traditional features because AI outputs are nondeterministic. The same user doing the same thing twice might get different outputs, introducing variance that traditional frameworks weren’t built to handle.

The core challenge is that intratreatment variance inflates the sample size you need for statistical significance, often by three to five times. If you’re running your AI experiment with normal sample size assumptions, you’re probably looking at noise and calling it signal.

Then there’s the metric selection problem. A chatbot generating entertaining but factually wrong responses might show amazing engagement numbers while actively misleading users. You have to measure engagement and quality together. “Engaged interactions where quality score exceeds threshold” is more meaningful than raw engagement alone.

The temporal problem matters too. AI feature value changes over time as users learn how to work with it. Short experiments will underestimate long-term value if there’s a learning curve, or overestimate it if there’s a novelty bump.

My practical guidance: budget two to three times more time and traffic for AI experiments than traditional ones. Lean on Bayesian methods as they handle high variance better. And always pair quantitative tests with qualitative research. Ten user interviews will surface failure modes that no amount of statistical analysis will catch.

Model drift monitoring

Model drift is the slow, invisible rot of AI output quality over time, and there are multiple culprits.

Data drift happens because the world changes and user behavior evolves. A model trained on 2024 data performs worse on 2026 queries referencing new concepts, slang, and cultural moments.

Provider drift happens because third-party APIs change without your consent. OpenAI acknowledged that GPT-4’s behavior shifted measurably between March and June 2023, and Stanford researchers documented significant performance swings. The fix: Pin your model versions so updates happen on your schedule, after your testing.

Evaluation drift is the subtlest form. Even your quality metrics can become inadequate and the evaluation criteria that made sense at launch might become inadequate as usage patterns shift and user expectations change. Quarterly reviews of your evaluation suites are essential.

At minimum you need daily automated quality evaluations on 1% to 5% of production traffic, weekly analysis of input distribution characteristics, and monthly human evaluation of 100 to 500 examples. Shipping an AI feature without drift monitoring is like deploying a service without alerting. You won’t know it’s broken until your users tell you, and by then they’re angry.

Evaluation frameworks

How do you know if your AI feature is good enough? You need two fundamentally different approaches, and you genuinely need both.

Automated evaluation gives you speed. Build a golden dataset of 500 to 2,000 labeled examples, train a classifier or use a capable model as judge, and validate against human judgment quarterly targeting 85% agreement. Automated evals chew through thousands of examples per hour, making them essential for velocity. The pitfall: They miss novel failure modes not in the training data.

Human evaluation catches what automation misses. Structure it with five to seven evaluators mixing domain experts and representative users. Use a consistent rubric covering accuracy, helpfulness, tone, completeness, and safety. Run weekly during development, monthly in production. The trade-offs: expensive at $15 to $30 per example, slow with 24 to 72 hour turnaround, and subject to human biases. Manage by rotating evaluators and capping sessions at two hours.

The model as judge approach is an increasingly viable middle ground. Judging quality is often easier than generating it, which means a model can reliably evaluate outputs even for tasks where it couldn’t produce them itself. Use it for high-volume evaluation but always validate against human judgment.

Graceful degradation and prompt engineering

Graceful degradation means when capabilities decrease, the experience gets worse smoothly instead of falling off a cliff. Design for capability levels, not binary states. Define four to five levels with specific behaviors at each. For example, for an AI writing assistant: Level 5 is full capability with real-time suggestions, tone adjustment, and structure recommendations. Level 4 is delayed suggestions appearing after a two- to three-second pause because latency is up. Level 3 is basic suggestions only like grammar and spelling with no style feedback. Each level is a deliberate design decision, not an accident.

Make degradation invisible when possible. Users shouldn’t see a “broken” experience. They see a less detailed one. That’s a huge difference psychologically. However,  when the degradation is significant enough that users will notice, proactive communication like “AI suggestions are temporarily limited” builds trust infinitely more than silently pushing poor-quality outputs.

Prompt engineering in production is software engineering. In production, prompts are code, and they need version control, testing, monitoring, and maintenance. Version controls every prompt. Parameterize prompts, don’t hardcode context. Production prompts should be templates with clearly defined injection points for user context, system state, and dynamic instructions. This makes them testable because you can inject known inputs and verify outputs, and it makes them maintainable because changing how you handle context shouldn’t require rewriting the entire prompt from scratch.

Test prompts against regression suites. Maintain 200 to 500 test cases covering the full distribution of expected inputs, including edge cases and adversarial inputs. Run the suite against every prompt change before deployment.

Monitor prompt performance in production. Track output quality metrics like acceptance rate, user edits, and regeneration requests, segmented by prompt version. When you deploy a new version, compare its production metrics against the previous one for at least 72 hours before calling it stable. This is basically canary deployment for prompts.

Ship it right

These systems aren’t optional add ons you can bolt on after launch. Every feature I’ve seen fail was built first with plans to “add production hardening later.” Later never comes.

AI features are probabilistic and nondeterministic, and they change over time without anyone touching them. Build these systems, staff them properly, and treat them with the same seriousness you’d give your core infrastructure. The gap between demo and production is wide, but it’s absolutely crossable if you build the right bridge.

Note: The research work pertaining to this article was done in a personal capacity. Views are of my own and do not reflect my employer’s views in any way.

On June 1, GitHub Copilot’s usage-based billing became active for all Copilot plans, and developers reacted quickly and loudly. A Pro plan still costs $10, but it now comes with a monthly pool of AI credits. Those credits are priced at a penny each, and they’re consumed according to the model used and the tokens processed, including input, output, and cached tokens. For a heavy agentic session running a frontier model, that makes spend feel very different from a flat subscription.

That’s the news, and it’s worth understanding, but it isn’t the important part. Nothing about the underlying cost of agentic work actually changed on June 1. The tokens were always being consumed, the loops were always running, and the tool calls were always expanding the context. What changed is that the meter became visible. A workload that had been quietly subsidized under a flat rate started showing up as an itemized bill.

Where the tokens go

To see why the bill landed so hard, it helps to compare two things that look similar and bill very differently. A chat completion is close to a single transaction. You send a prompt, the model sends an answer, and you pay roughly once for the input and once for the output. A tool-using agent doesn’t work that way at all. An agent doesn’t answer a question so much as work toward it, and it works by looping. It reasons about the task, calls a tool, reads the result, reasons again, calls another tool, and continues until it decides it’s finished.

Every pass through that loop carries a cost that’s easy to miss. In many agent harnesses, each turn carries forward a large share of the accumulated context: prior messages, tool descriptions, retrieved files, and tool results. Even when some of that context is cached, summarized, or pruned, the system is still doing metered work to preserve enough state for the next decision. The final answer you actually wanted is only a thin slice of what you paid for. The loop is the bill.

This is why agent cost doesn’t scale politely. It scales with the number of turns, and the number of turns scales with how much discovery the agent has to do, which in turn scales with how vague the request was and how much irrelevant context it’s dragging along. A clean, well-scoped task might finish in three turns, while the same task posed as an open-ended question might wander through 15, each carrying the cost of everything that came before it. Under a flat rate, that difference was invisible. Under usage-based billing, it’s the difference between a small interaction and an expensive one.

Tool design is now part of the cost model

I wrote recently about a hidden tax on Model Context Protocol servers: the way an overstuffed tool catalog quietly degrades a model’s ability to route to the right tool. Bloated descriptions, overlapping responsibilities, and vague parameters make the model’s job harder and its choices worse. That argument was about accuracy. The billing change adds a second invoice for the same bloat, and this one is denominated in dollars.

The tool catalog is often part of what gets carried through the agent’s loop. A tool described in three tight sentences and a tool described in three rambling paragraphs may both function, but the second one pays rent in the context window every time an agent has it loaded. Multiply that across a catalog of 40 tools and a workflow that runs a dozen turns, and the cost of verbose tool design stops being a rounding error. Tool design was already a correctness discipline. It’s now a cost discipline as well. The same audit that tightens routing accuracy tightens the bill.

Where prompt discipline runs out

There’s a layer of this that individual users can control, and it’s worth knowing because the savings are real and immediate. Two patterns matter most, and I’ve been handing both to the engineers on a pilot I run for a large healthcare organization. They aren’t magic tricks. They’re ways to keep the agent out of unnecessary discovery loops.

The first pattern is about input. Prompt the agent like a short requirement rather than a broad question. A request such as “look at the encounter data and tell me what you find” forces the agent into discovery mode, where it burns turns figuring out what you meant, and every one of those turns carries the full context forward. Compare that to a prompt that front-loads the specifics by naming the project and the table, naming the date field to filter on, stating the output shape you want, and calling out anything that should be excluded. A better prompt would be: “Using the curated clinical project and the silver-zone encounters table, show total encounters by month for calendar year 2025, use admission_date_time for inclusion, and return one row per month ordered chronologically.” The second prompt collapses the loop. The agent has what it needs on the first turn, so it does the work instead of interviewing you for it.

In practice, the difference isn’t just polish. The vague version forces the agent to discover the data model, infer the date semantics, choose an aggregation, and decide on a display format. The specific version turns the task into a bounded query. That difference shows up in accuracy, latency, and cost.

The second pattern is about output, and it’s the lever most people overlook. Ask for plain text or Markdown during the intermediate steps, and save rich HTML formatting for the final, confirmed deliverable. Formatted output is expensive to generate, and requirements shift. If you ask for a polished HTML report on the first pass and then change a filter, you pay full output-token freight to regenerate all that layout, often more than once. The cheaper habit is to validate the numbers in text and format only at the end.

These patterns work, and they also have a ceiling. Both of them put the entire burden of cost control on the user, and they hold only as long as every user exercises the discipline on every prompt. The day someone reverts to “tell me what you find,” the savings evaporate, and the only thing standing between the team and a surprise invoice is a budget cap that reports the overspend after it has already happened.

Cost is a governance problem, not a budgeting one

That fragility is the real lesson. A budget cap is a backstop rather than a control. It will stop a runaway, but it tells you that you overspent rather than why, and it does nothing to make the next run cheaper. Treating cost as a budgeting problem leaves you forever reacting to the meter, while treating it as an architecture problem lets you build the savings in once and stop relying on everyone’s good behavior.

That means the controls that matter belong on the platform rather than in individual prompts. By the platform I don’t mean the agent itself, the coding assistant or chat client a developer drives day-to-day, and I don’t mean the model or a router sitting beneath it. I mean the control plane that sits above the agents, the layer where an organization enforces policy, access, observability, and now cost across every agent and model its developers touch. An administrative console that gives IT visibility into who is doing what and which capabilities they can install is an early, narrow instance of it. A router that sends planning to a cheap model is one feature that belongs there. The platform is where the rules live, and the agent is a consumer of those rules rather than the place you set them. The platform should route models by task, using cheaper models for planning and reserving frontier models for work that earns the price. It should bound the loop, requiring the agent to check in after a fixed number of iterations. It should cap tool-result payloads so a careless query cannot dump a million rows into the context window. It should default intermediate work to plain text, making the cheap path the path of least resistance instead of something users have to remember.

Every one of those controls is something a user can approximate by hand and something the platform can simply guarantee. This is the same principle I keep returning to in the context of data access, where safe behavior cannot depend on the person at the keyboard remembering the rules. Prompts guide behavior. Guardrails make the cheaper and safer behavior the default. Cost governance is guardrails as control plane, with a dollar sign attached, enforced at the same layer where you already enforce who is allowed to see which row.

The pattern, not the vendor

It would be a mistake to read this as only a GitHub story. GitHub is the current example because its change is visible and recent, but usage-based billing for agentic work is the direction of travel for many AI tools. The economics under the hood are similar: Agentic workloads turn single answers into loops of model calls, tool calls, and context management. The flat-rate subsidy was always going to come under pressure once the workload shifted from autocomplete to autonomy.

The organizations that treat June 1 as a pricing event will optimize a few prompts, grumble, and move on until the next vendor changes its meter. The ones that treat it as an architecture signal will push the cost controls down into the platform, where they hold regardless of which provider is counting which token. That’s the more durable place to stand. The bill didn’t get bigger this month. It got honest, and an honest bill is the kind you can engineer against.

The following article originally appeared on Addy Osmani’s blog and is being reposted here with the author’s permission.

A long-running AI agent can keep making progress over hours, days, or weeks. It can do this across many context windows and sandboxes, recover from failure, leave structured artifacts behind, and resume where it left off.

For two years the dominant image of an “AI agent” has been a chat window with a clever loop in it. You type a goal; the agent calls some tools; you watch tokens stream by; you stop watching when the work runs out of patience or the context window fills up. That paradigm got us a long way, but it has a ceiling. The model forgets. It declares “task complete” when it isn’t. It reintroduces a bug it fixed nine turns ago. The whole thing is structured around a single sitting.

Long-running agents are what comes next. The idea is easy to state: an agent that keeps making forward progress on a goal across many sessions and many sandboxes, possibly many days or weeks, while leaving the workspace clean enough that the next session can pick up where the last one left off. The engineering is harder. You have to solve for persistence, recovery, and verification in a way that doesn’t just paper over the cracks. You have to build a state layer that lives outside the model’s context window, and you have to design the handoff between sessions so the agent doesn’t lose its mind when it wakes up and finds itself in a different sandbox with a different context window.

This post is my attempt to lay out what’s changed, who’s pushing on it, and how an engineer can use long-running agents today without writing the whole thing from scratch.

What “long-running” actually means

“Long-running” used to mean at least three different things in practice, and it helps to keep them separate.

Long-horizon reasoning. The agent has to plan and execute over many dependent steps. This is mostly a model-quality story: coherence, planning, the ability to recover from a wrong turn 10 steps ago. METR has been tracking this with their time horizon metric, which estimates how long a task a frontier model can complete with 50% reliability. The headline finding is that the metric has been doubling roughly every seven months since 2019, and their TH1.1 update earlier this year doubled the count of eight-hour-plus tasks in the eval set. If that curve holds, frontier agents complete tasks at the day scale by 2028 and the year scale by 2034.

Long-running execution. The agent’s process runs for hours or days. Maybe it’s a coding job, maybe it’s a research sweep, maybe it’s a 24-7 monitoring service. The model might be invoked thousands of times across the run. This is mostly a harness story, and it’s the one this post is mostly about.

Persistent agency. The agent has an identity that outlives any single task. It accumulates memory, learns user preferences, and is always available. This is the Memory Bank flavor of long-running.

In practice the three blur together. A real production agent does long-horizon reasoning inside a long-running execution backed by persistent agency. But the engineering problems are different in each, and so are the products that solve them.

Why this matters

There are two reasons I believe this work matters a lot right now.

The first is a phase change in what’s economically feasible to delegate. An agent that runs for 10 minutes can answer a question, summarize a doc, fix a small bug. An agent that runs for 10 hours can own an entire feature, finish a migration that was on the backlog for six quarters, or do the kind of overnight research sweep that used to require a junior analyst. One of Anthropic’s Claude Sonnet announcements put concrete numbers on this last fall: 30+ hours of autonomous coding in internal tests, including one run that produced an 11,000-line Slack-style app. That’s already past the threshold where the answer to “Should I delegate this?” is no longer obvious.

The second is that persistence changes what the agent is. A stateless agent answers your question and disappears. A long-running one accumulates context: which competitor moved which way last week, which test flaked twice on Tuesday, what you usually mean by “the dashboard.” Anthropic’s Project Vend was the most public early demonstration of this. They had a Claude instance run an actual office vending business for a month, managing inventory, setting prices, talking to suppliers. It failed in informative ways, and the second phase ran much better, but the point wasn’t profitability. The point was watching what kinds of weird coherence problems show up when an agent has to maintain identity across weeks instead of turns.

Those are the same problems every team building production agents now hits.

The three walls every long-running agent hits

Three walls show up in basically every write-up I’ve read this year.

Finite context. Even a 1M-token window fills. And context rot, the steady degradation of model performance as the window gets full, kicks in well before the hard limit. A 24-hour run is not going to fit in any context window the field has on its roadmap. Something has to give.

No persistent state. A new session starts blank. Anthropic’s framing in their scientific computing post is the cleanest version I’ve seen: “Imagine a software project staffed by engineers working in shifts, where each new engineer arrives with no memory of what happened on the previous shift.” Without an explicit persistence story, every shift change is a productivity disaster.

No self-verification. Models reliably skew positive when they grade their own work. Asked “Are you done?” they answer “yes” more often than they should. Without a separate signal that the work meets a bar, you get the agent that ships at 30% complete with full confidence.

Long-running agent designs are mostly answers to these three problems. The major labs have converged on similar shapes of answer, but with very different surface area.

The Ralph loop: One of the simpler practitioner versions of long-running agents

The Ralph loop (sometimes called the Ralph Wiggum technique) is one of “simpler” practitioner version of long-running agents, popularized by Geoffrey Huntley and Ryan Carson. The reference implementation is literally a bash script that loops:

1. Pick the next unfinished task from a list (prd.json or equivalent).
2. Build a prompt with the task, the relevant context, and any persistent notes.
3. Call the agent.
4. Run tests or other checks.
5. Append what happened to progress.txt.
6. Update the task list (done, failed, blocked).
7. Go back to step 1.

The reason it works is the same reason any of the harnesses below work: State lives outside the agent’s context. prd.json is the plan, progress.txt is the lab notes, and AGENTS.md is the rolling rulebook. The agent itself is amnesiac, but the filesystem isn’t. Each iteration starts fresh and reads enough state from disk to keep going. Carson’s Compound Product extends the idea by chaining multiple loops (an analysis loop that reads daily reports, a planning loop that emits a PRD, an execution loop that writes the code), which is roughly the open source version of the planner-generator-evaluator triad Anthropic landed on independently.

I went deeper on all of this in “Self-Improving Coding Agents”: task list structure, progress files, QA gates, monitoring, the failure modes you’ll actually hit. The short version is that you can build a working long-running agent in an evening with a bash script and a JSON file. Most of what Google and Anthropic have productized is the work of making this pattern recoverable, secure, and observable at scale.

The big-lab stories below are different ways of paying for that production-readiness.

Anthropic: Harnesses, then the brain/hands/session split

Anthropic has been the most public about the engineering. Two posts are worth reading end to end.

The first is “Effective Harnesses for Long-Running Agents,” which lays out a two-agent harness for autonomous full stack development. An initializer agent runs once at the start of a project to set up the environment, expand the prompt into a structured feature-list.json, and write an init.sh that future sessions will run on boot. A coding agent is then woken up over and over, each session asked to make incremental progress on one feature, run tests, leave a claude-progress.txt note, and commit. A test ratchet (“it is unacceptable to remove or edit tests because this could lead to missing or buggy functionality”) sits in the prompt to stop the very common failure of an agent deleting failing tests to “make them pass.” InfoQ’s writeup extends this into a planner, generator, and evaluator triad, on the same logic that separating generation from evaluation matters because models grade their own work too generously.

The second is “Scaling Managed Agents: Decoupling the Brain from the Hands,” the architectural post behind Claude Managed Agents (Anthropic’s hosted runtime, launched in early April). The argument is that an agent has three components that should be independently replaceable. The Brain is the model and the harness loop that calls it. The Hands are sandboxed, ephemeral execution environments where tools actually run. The Session is an append-only event log of every thought, tool call, and observation.

This sounds abstract, but it isn’t. Here’s Anthropic’s framing: “Every component in a harness encodes an assumption about what the model can’t do on its own.” When you couple them, an assumption that goes stale (e.g., the model used to need an explicit planner and now plans natively) means the whole system has to change at once. When you decouple them, the harness becomes stateless, sandboxes become cattle, not pets, and a brain crash doesn’t lose the run. A fresh container calls wake(sessionId) and reconstitutes the state from the log. They reported time-to-first-token dropped ~60% at p50 and over 90% at p95 just from being able to start inference before the sandbox is ready.

The session-as-event-log idea is the part most teams underappreciate. It is what makes a long-running agent recoverable. Without it, a container failure is a session failure and you’re debugging into a stale snapshot. With it, the agent’s memory is a queryable artifact that lives outside whatever process happens to be running at the moment.

For the scientific computing crowd, Anthropic’s “long-running Claude” post reduces all of this to a simpler stack: CLAUDE.md as a living plan the agent edits as it learns, CHANGELOG.md as portable lab notes, tmux plus SLURM plus git as the execution and coordination layer, and the Ralph loop, a for loop that kicks the agent back into context whenever it claims completion and asks if it’s really done. Their flagship case study is a Boltzmann solver Claude Opus 4.6 built over a few days that reached subpercent agreement with a reference CLASS implementation. Months to years of researcher time, compressed.

Same patterns across all three posts: an explicit plan file, an explicit progress file, structured handoffs between sessions, separate generation from evaluation, and a loop that refuses to let the agent stop early.

Cursor: Planners, workers, judges

Cursor’s “Scaling Long-Running Autonomous Coding” is the other essential read this year. They walked into walls that Anthropic mostly papered over.

Their first attempt was a flat coordination model: equal-status agents writing to shared files with locks. It became a bottleneck and made the agents risk averse, churning rather than committing. Their second attempt swapped locks for optimistic concurrency control, which removed the bottleneck but didn’t fix the coordination problem. The third design is what’s running in production now and what they describe as solving most of the problem:

• Planners continuously explore the codebase and emit tasks. They can recursively spawn subplanners.
• Workers are focused executors. They don’t coordinate with each other and they don’t worry about the big picture.
• Judges decide when an iteration is finished and when to restart.

Two things stand out from the post. One: “A surprising amount of the system’s behavior comes down to how we prompt the agents” more than the harness or the model. Two: Different models slot into different roles. Their reported finding is that a GPT model was better than Opus for extended autonomous work specifically because Opus tended to stop early and take shortcuts. Same task, different role, different model. The matching is becoming part of the design surface.

This pairs with Composer 2 (their proprietary frontier coding model that ships in Cursor 3) and their background cloud agents: long-running tasks that run on Anysphere’s cloud infrastructure rather than your laptop. Eight-hour refactors and codebase-wide migrations survive a closed lid. You can start a task locally, hit run in cloud when you realize it’ll take 30 minutes, and reattach later from your phone. Each agent runs in an isolated Git worktree and merges back via PR. The handoff between local and remote is the part most teams haven’t figured out yet, and Cursor’s bet is that it has to be its own product surface.

The shape ends up close to Anthropic’s: Roles are split, sessions are durable, judges sit beside the worker, and a long task runs in a cloud sandbox with Git as the coordination substrate.

Google: Long-running agents on the Agent Platform

Google’s announcement at Cloud Next ’26 folded Vertex AI into the Gemini Enterprise Agent Platform and turned long-running agents into a named product, with named SLAs.

The pieces that matter for this post:

• Agent Runtime supports agents that “run autonomously for days at a time” with sub-second cold starts and on-demand sandbox provisioning. The launch post’s example use case is a sales prospecting sequence that takes a week to play out, which is roughly the right shape for it.
• Agent Sessions persist conversation and event history. You can pin them to a custom session ID that maps to your own CRM or DB record, so the agent’s state lives next to the business state instead of in a separate AI silo.
• Agent Memory Bank is the persistent long-term memory layer, generally available as of Next ’26. It curates memories from sessions, scopes them to a user identity, and exposes a search API so the next agent invocation can pull what’s relevant. Payhawk reported that auto-submitting expenses through a Memory Bank-backed agent cut submission time by over 50%.
• Agent Sandbox handles hardened code execution.
• Agent-to-Agent Orchestration, Agent Registry, Agent Identity, Agent Gateway, Agent Observability, and Agent Simulation cover basically every operational concern you’d otherwise build by hand for a production fleet, including the cryptographic-identity-and-audit-log story enterprises actually need to ship.

Architecturally this is the same brain/hands/session split Anthropic described, just productized at platform scale and bundled with ADK (the code-first dev kit) and Agent Studio (the visual one). If you’re building inside Google Cloud, you don’t have to design a session log or a memory store from scratch anymore. You wire an ADK agent into Memory Bank and Sessions, deploy onto Agent Runtime, and the persistence question is answered.

Notice how much this looks like the pattern Anthropic and Cursor describe, just unbundled into named services with SLAs. Three years ago you’d have built all of this yourself. Now you pick which version of “decoupled brain, hands, and session” you want to rent.

Five patterns for long-running agents in production

Shubham Saboo and I wrote up five design patterns we’ve seen separate working long-running agents from demos. They aren’t Google-specific, but they map cleanly onto the primitives Agent Runtime now exposes, so it’s worth walking through them here in shortened form.

Checkpoint-and-resume. The most common multiday failure is context loss. An agent processes 200 documents over four hours, hits an error on document 201, and without a checkpoint you start from scratch. Treat the agent like a long-running server process: write intermediate state to disk, checkpoint every N units of work, recover from failures. The Agent Runtime sandbox gives you a persistent filesystem, but choosing the right checkpoint granularity (not every step, not only the end) is on you.

Delegated approval (human-in-the-loop). Most “human-in-the-loop” implementations are: serialize state to JSON, fire a webhook, hope someone responds. The state goes stale, the notification gets buried, the agent re-deserializes into a slightly different world. Long-running runtimes let the agent pause in place with full execution state intact: reasoning chain, working memory, tool history, pending action. Hours of human time pass, the agent consumes zero compute, and it resumes with subsecond latency. Mission Control is Google’s inbox for this. The pattern works regardless of vendor.

Memory-layered context. A seven-day agent needs more than session state. Memory Bank handles long-term curated memory, Memory Profiles add low-latency lookups, and the failure mode you’ll hit in production is memory drift: The agent learns a procedural shortcut from a few atypical interactions and starts applying it broadly. Govern memory like you govern microservices. Agent Identity controls who can read and write which banks. Agent Registry tracks which version of which agent is running. Agent Gateway enforces policy on the wire. The auditing question stops being “What are my agents doing?” and becomes “What are my agents remembering, and how is that changing their behavior?”

Ambient processing. Not every long-running agent talks to a human. Some sit on a Pub/Sub stream or a BigQuery table and act on events as they arrive: content moderation, anomaly detection, inbox triage. The architectural decision worth making early is to not hardcode policy into the agent. Define it in the Gateway and the fleet picks up policy changes without redeploys. Ambient agents run unsupervised for long stretches, and the only sane way to update a hundred of them is to update the policy layer once.

Fleet orchestration. In real systems, you rarely have one agent. A coordinator delegates subtasks to specialists (a Lead Researcher Agent, a Scoring Agent, an Outreach Agent), each running independently for different durations. Each specialist gets its own Identity (so the Outreach Agent can’t read financial data meant for Scoring), its own policy enforcement, its own Registry entry. This is the same coordinator/worker shape distributed systems have used for decades. What’s new is that ADK handles it declaratively with graph-based workflows, and a bad deployment in one specialist doesn’t cascade to the others.

The patterns compose. A compliance system might use checkpointing for document processing, delegated approval for review gates, memory layering for cross-session knowledge, and fleet orchestration to coordinate the specialists. The opening question is always the same: What’s the longest uninterrupted unit of work your agent needs to perform? Minutes, and you don’t need long-running agents. Hours or days, and these patterns are where to start. The full write-up with code samples covers each pattern in depth.

So how do you actually build one today?

This is the practical question, and it has a different answer depending on what you’re building.

You’re a developer who wants long-running coding work on your own repo. Just use Claude Code (or Antigravity, Cursor, or Codex). The harness is already there. Treat your AGENTS.md like a pilot’s checklist: short, every line earned by a real failure. Add hooks for typecheck and lint that surface failures back to the agent. Write a plan file before the agent starts. Use the Ralph loop when the agent claims it’s done and you don’t believe it. For multihour or overnight jobs, run in a worktree so a closed laptop doesn’t kill the run, and have it commit progress every meaningful unit of work. This is the path most people should take, and it’s where the most leverage is right now.

You’re building a hosted agent product. Don’t build the runtime. Pick a managed one. The three real options today: Google’s Agent Platform (Agent Engine + Memory Bank + Sessions), Claude Managed Agents, or roll something on top of ADK, the Claude Agent SDK, or Codex SDK and host it yourself. The trade-off is the usual one. Managed gets you the brain/hands/session split, observability, identity, and an audit trail out of the box. Self-hosted gets you control and the ability to use weird models for weird roles (Cursor’s pattern). For most teams, the right starting point is a managed runtime plus your own ADK or SDK code for the actual loop.

You’re doing something autonomous and operational (monitoring, research, ops). Memory Bank-style persistence is what you want, and it’s the part that doesn’t exist in Claude Code. ADK + Memory Bank + Cloud Run + Cloud Scheduler is the cleanest stack I’ve seen for “agent runs every N hours, accumulates state, alerts on a threshold.” This is also where Cursor’s planner/worker/judge split starts to matter more than it does for IDE coding, because the work is genuinely parallel and the failure modes are different.

A few things matter regardless of which path you take.

Write down the done condition before the agent starts. This is the single highest-leverage move for long runs. The Anthropic harness post calls it the feature list; Cursor calls it the planner’s task spec. Either way, it’s an external file with explicit, testable completion criteria, and it exists so the agent can’t quietly redefine done midrun.

Separate the evaluator from the generator. Self-grading is the failure mode. A planner/worker/judge pipeline, or a generator/evaluator pair, is a real architectural pattern, not a stylistic preference. Even if it’s the same model in different roles with different prompts.

Invest in the session log, not just the prompt. The append-only event log is what makes the agent recoverable, debuggable, and auditable. If you can’t reconstruct what the agent did in the last 24 hours from durable storage, what you have is a long-running shell script that happens to call an LLM, not a long-running agent.

Treat compaction and context resets as first class. Anthropic is explicit that summarization-as-compaction wasn’t enough for very long jobs; they had to do full context resets where the harness tears the session down and rebuilds it from a structured handoff file. It is essentially how humans onboard a new engineer.

There are some real limitations right now

A few things are still genuinely unsolved.

Cost. A 24-hour run with a frontier model and a few tools is not cheap. Without budgets, circuit breakers, and a hard cap on tool spend, an agent can quietly burn through a week’s API budget in an afternoon. This is solvable, but it’s an explicit step you have to take.

Security. A long-running agent with API keys, cloud access, and the ability to run shell commands has a much larger attack surface than a chat session. The brain/hands separation pattern matters here too: Credentials should be unreachable from the sandbox where model-generated code runs, which is one of the benefits Anthropic calls out for Managed Agents.

Alignment drift. Over many context windows, agents drift. The original goal gets summarized, then resummarized, then loses fidelity. This is the part hooks and judges exist to defend against. It is also the most common reason “the agent went off and did something I didn’t ask for.”

Verification. Auditing 24 hours of autonomous activity is a real human-time problem. Observability and structured artifacts (PRs, commits, briefings, test runs) are how you make this tractable. Without them, you’re scrolling logs and you’ll miss what matters.

The human role. This is the one I keep coming back to. Defining work crisply enough that an agent can run for a day on it is harder than doing the work yourself. The skill that’s appreciating in value isn’t writing code. It’s writing specs that survive contact with an autonomous executor.

Where this is going

Google, Anthropic, and Cursor have converged on roughly the same shape. Separate the model loop from the execution sandbox from the durable session log. Split planning from generation from evaluation. Bake in compaction, hooks, and context resets. Expose memory as a managed service that any agent invocation can query.

Surface area is what differs. Google’s Agent Platform is the enterprise-stack version, with the identity and audit trail story baked in. The patterns underneath are the same. Claude Managed Agents is “Anthropic’s harness, hosted.” Cursor’s background agents are “long-running coding, pulled out of the IDE and into the cloud.”

The harder problems for the next year aren’t in any of those layers individually. They’re in the coordination above them. Many long-running agents on a shared codebase. Agents that read their own traces and patch their own harnesses. Harnesses that assemble tools and context just in time for a task instead of being preconfigured at startup. That’s where the agent stops looking like a smarter chat window and starts looking like a colleague who’s been on the project longer than you have.

The model is still load-bearing. But the gap between a chat window and an agent you can leave running overnight is mostly in the state, sessions, and structured handoffs wrapped around it. That’s where I’d spend my learning time right now.

The following article originally appeared on Paolo Perrone’s The AI Engineer Substack and is being reposted here with the author’s permission.

Your team picks LangGraph for a customer support chatbot. Three weeks in, you’ve got 14 nodes in a state graph, a custom checkpointer writing to Redis, and retry logic for tool calls that fail once a week. The agent answers refund questions. It calls one API. A 50-line script on the OpenAI SDK with two MCP servers would have done the same thing. But nobody mapped which layers the problem actually needed.

In November 2024, Letta published an AI agents stack diagram that became the default reference for half the engineering teams I talk to. If you’ve seen a “layers of an agent” visual on LinkedIn or pinned in a Slack channel, it probably traces back to that article.

That diagram is 14 months old now, and a lot has changed since. MCP didn’t exist yet. Memory was still treated as a subset of your vector database. Nobody was shipping provider-native agent SDKs. Eval wasn’t even on the map. The stack has six layers in 2026, and at least three of them didn’t exist as distinct categories when Letta drew the original.

So we drew it from scratch. This is the 2026 version.

TL;DR

That’s the starting stack. Add complexity when something specific breaks, not before.

What are we even mapping?

Before the stack, there was a loop. In “What Is an AI Agent?,” we defined an agent as the think-act-observe cycle: The model reasons about a task, takes an action (calls a tool, writes to memory), observes the result, and loops until the task is done. That loop is the atomic unit. Everything in this issue is infrastructure that makes that loop work reliably, at scale, in production.

The agent stack is not the LLM stack. A chatbot needs inference and maybe RAG. An agent needs state management across multistep execution, tool access governed by protocols, memory that persists across sessions, autonomous reasoning loops, and guardrails that constrain behavior in real time. That’s a fundamentally different set of infrastructure problems.

We’re mapping the six layers between your LLM and a production agent. We’re not covering training infrastructure, data pipelines, or model fine-tuning. Those are adjacent stacks. We covered RAG in depth in Issue #5. Today we’re zooming out to show where RAG fits in the bigger picture.

Three things redrew the map between 2024 and 2026. MCP standardized tool connectivity, and the entire tools layer is new because of it. Reasoning models changed what agents can do autonomously, with single-call agents replacing some multistep chains. And memory became a first-class architectural primitive, not an afterthought bolted onto a vector database.

How to evaluate each layer

When choosing tools at each layer, ask three questions. How much state do you need to manage? A stateless tool caller and a multi-session agent that learns over time are different engineering problems, and the layers where state management is hardest (memory, frameworks) are where most teams get stuck. How much vendor lock-in can you tolerate? MCP is an open standard, provider SDKs are not, and every tool choice either increases or decreases how painful your next migration will be. And how hard is it to go from demo to production? Some layers (model serving) have almost no gap, while others (eval, guardrails) have a massive one. The layer where you feel that gap most is the one to invest in first.

We take each layer from the bottom up, starting with the most stable and ending with the least mature.

Layer 1: Models and inference

How you run the model that powers your agent: call an API, use a managed open weight provider, or self-host.

The inference layer changed more in tone than in substance. Reasoning models like o1, o3, DeepSeek R1, and Claude with extended thinking shifted what agents can plan and execute. Agents that previously needed multistep chains can now solve problems in a single reasoning call. Open weight models like Llama 3.3, DeepSeek V3, and Qwen 2.5 closed the quality gap dramatically, so “always use the biggest closed model” is no longer default advice. The emerging pattern is to prototype on closed source and deploy on open weight.

The honest take: This layer is commoditizing. Model differences matter less each quarter. The real decision is the cost and latency trade-off, not which model is “smartest.”

On the evaluation side, API calls are stateless. Send a request, get a response. Nothing to manage. Lock-in risk runs high for closed APIs because each model reasons differently, so switching providers means retuning prompts, adjusting for different failure modes, and retesting your eval suite. It’s low for open weight, where you can swap the model and keep the infra. The prototype-to-production gap is the smallest of any layer. Your demo API call is the same as your production API call.

Self-host when your agent call volume makes API pricing untenable or when you need sub-100ms latency that API round-trips can’t deliver.

Layer 2: Protocols and tools

How your agent calls external tools and APIs: through MCP servers, browser automation, or agent-to-agent protocols.

This layer didn’t exist as a distinct category in 2024. Every framework had its own JSON schema for tool definitions. Now MCP is the standard, with 97M monthly SDK downloads, adoption by OpenAI, Google, and Microsoft, and a donation to the Linux Foundation.

Browser Use exploded in parallel, hitting 78K GitHub stars in under a year. Nobody was shipping browser agents in production in 2024. And agents can now talk to other agents. IBM launched ACP, and Google launched A2A. Neither is standard yet, but the problem they solve (agents coordinating with other agents) is real and growing.

Security is the open problem. Endor Labs analyzed 2,614 MCP servers and found 82% prone to path traversal and 67% to code injection.

The honest take: The protocol debate is over. MCP won. The only question left is how you lock down your MCP servers before someone exploits them.

State management is nonexistent here. Your agent calls a tool, gets a response, done. No session, no memory between calls. Lock-in risk is low because MCP is an open standard, so if you build MCP servers, any MCP-compatible agent can use them. The prototype-to-production gap is medium. Your demo MCP server works until someone sends a malicious tool description. Security and governance are the gap.

MCP standardized how agents use tools. It says nothing about how agents talk to each other. ACP and A2A are trying to solve that, but neither has reached critical mass. If you need multi-agent coordination today, you’re building it yourself at the framework layer. We covered MCP in depth in Issue #4.

Layer 3: Memory and knowledge

How your agent stores and retrieves what it knows: in-context state, vector search, or persistent memory across sessions.

All three tiers feed into the same place: The context window your agent sees on every call.

In 2024, memory meant “pick a vector database and do RAG.” In 2026, memory is a first-class architectural primitive with three distinct tiers. Context windows got massive. Gemini hit 1M+ tokens, Claude 200K. Bigger windows didn’t kill the need for memory. They changed the trade-off: What do you stuff in-context versus what do you retrieve on demand?

“Context engineering” replaced “prompt engineering” as the core discipline. Instead of writing a better prompt, you architect what information the agent sees on every call. Memory blocks appeared as named, structured fields in the context window that the agent can read and overwrite every turn. Instead of dumping everything into the system prompt, the agent manages its own state: what to keep, what to update, what to drop.

On the infrastructure side, pgvector became the default for teams that don’t need a dedicated vector database. It’s just Postgres with an extension. GraphRAG emerged as a second retrieval option: follow relationships between entities instead of matching embeddings, with Neo4j leading this space. Sleep-time compute, where agents process information during idle time, is research stage but signals where tier 3 is heading.

The honest take: Most teams overcomplicate memory. Start with conversation history in Postgres and a structured system prompt. Add vector search when your history exceeds context limits. Add agentic memory management only when your agent needs to learn across sessions.

This IS the state layer. You’re deciding what your agent remembers, how it retrieves it, and when it forgets. Highest complexity in the stack. Lock-in risk is medium. pgvector is portable because it’s just Postgres, while specialized tools like Mem0 or Zep are harder to migrate away from. The prototype-to-production gap is large. Demo memory works because context windows are big enough. Production memory breaks when conversations get long and your agent starts forgetting the important parts.

In-context memory breaks down when agents need to share memory across instances or maintain state across model provider switches. That’s where dedicated memory infrastructure like Letta, Zep, and Mem0 earns its keep.

Layer 4: Frameworks and SDKs

How you wire together the model calls, tool use, and control flow that make your agent work: a provider’s built-in toolkit (SDK), a graph-based framework like LangGraph, or raw code.

Every major AI lab now ships its own agent SDK. OpenAI has the Agents SDK (evolved from Swarm). Google released ADK. Microsoft has Semantic Kernel and AutoGen. Hugging Face built smolagents. Two years ago, LangChain was the only game. Now you pick between three camps: provider SDKs that are fast to start but locked to one model, graph-based frameworks like LangGraph that are portable but require more setup, or no framework at all. That choice didn’t exist in 2024.

LangGraph solidified as the graph-based orchestration leader with v1.0 released October 2025 and production deployments at Uber, JPMorgan, LinkedIn, and Klarna. LangChain agents are now built on LangGraph under the hood. Meanwhile, the “build it yourself” camp grew. Teams that tried LangChain in 2024 and fought the abstraction are now writing thin wrappers over provider APIs + MCP. No framework means full control. This works until your agent needs state management or complex branching.

A quick note on naming: “LangChain” and “LangGraph” are not the same thing. LangChain is the integration layer handling model connectors, tool calling, and prompt templates. LangGraph is the orchestration engine managing state, control flow, and graphs. Most production teams use both together, but LangGraph is where the agent logic lives.

The honest take: Most teams pick too much framework. If your agent calls a model and a few tools, you don’t need LangGraph. A provider SDK and a couple of tool calls will get you to production faster than any graph.

Provider SDKs manage state for you. LangGraph makes you define every state transition explicitly. Build-it-yourself means you roll your own. Lock-in risk is the highest in the stack. Your orchestration code doesn’t port. A LangGraph agent rewritten for CrewAI is a new codebase. Provider SDKs are worse because you’re locked to one model too. The prototype-to-production gap is large. Demo works because nothing goes wrong. Production means handling tool failures, retries, timeouts, and humans who need to approve before the agent acts.

The framework you pick determines your migration cost. Provider SDKs are fastest to start but lock you to one model. LangGraph is portable but complex. Building your own gives you full control until your agent outgrows your wrapper. MCP is the one layer that transfers across all three camps.

Layer 5: Eval and observability

How you measure whether your agent is doing its job: tracing runs, scoring outputs, and catching regressions before users do.

This layer barely existed in 2024. Now it’s the gap. LangChain’s State of Agent Engineering survey found 89% of teams with production agents have implemented observability, but only 52% have evals. That 37-point gap is where production quality dies.

“Evaluation as infrastructure” is converging on three tiers: fast checks on every PR (Did the agent call the right tools?), nightly regression suites that use an LLM to judge output quality, and continuous production monitoring that alerts when agent performance drifts. New agent-specific benchmarks have emerged too, including Context-Bench for memory management, Recovery-Bench for error recovery, and Terminal-Bench for coding agents.

The honest take: Most teams skip eval until something breaks in production. By then they’re debugging blind. The teams that don’t have this problem built evals before they deployed.

State management matters here because your agent runs 12 steps, step 3 picked the wrong tool, and steps 4–12 were doomed from there. If your eval only checks the final output, you’ll never know why. Lock-in risk is moderate. Most tools export OpenTelemetry traces, so switching observability providers is doable, but switching eval frameworks means rebuilding your test suites. The prototype-to-production gap is the biggest of any layer. Most prototypes have zero eval. You don’t feel the pain until production users find the failures for you.

Current eval tools are strongest for single-turn and tool-calling evaluation. Multi-agent evaluation, long-horizon task assessment, and evaluating agents that learn over time are all unsolved problems. If your agent does any of those, you’ll need custom eval infrastructure beyond what the platforms offer today.

Layer 6: Guardrails and safety

How you stop your agent from doing things it shouldn’t: filtering inputs, authorizing tool calls, and validating outputs.

Agent guardrails became a separate discipline from LLM guardrails. In 2024, guardrails meant input/output filters on a model. In 2026, your agent calls tools, spends money, and takes actions. Guardrails now means authorizing tool calls, enforcing rate limits, and validating what the agent actually did.

The “guardrails before action” pattern emerged from teams that learned the hard way. They now enforce authorization at the tool execution layer, not the output layer. By the time you filter the response, the agent already sent the email. OWASP published the MCP Top 10 (beta), which is the first real security checklist for tool-connected agents. Deployment is still DIY. LangGraph Cloud and Bedrock Agents exist, but most production teams are still deploying with FastAPI and their own infra. This layer is where you’ll spend the most unplanned engineering time.

The honest take: This is the least mature layer in the stack. No dominant framework, no established patterns. You’re writing policy code from scratch.

Guardrails need to know what the agent is doing right now to decide what it shouldn’t do next. That means tracking agent state in real time. Lock-in risk is low because most guardrails are custom policy code you write yourself. NeMo Guardrails is the closest thing to a framework, but you’ll still write most rules from scratch. The prototype-to-production gap is effectively infinite. Your demo has no guardrails because nobody’s trying to break it. Production will.

Current guardrails tools focus on single-agent systems. If you’re running multi-agent workflows where agents delegate to each other, guardrail propagation across agent boundaries is an unsolved problem. You’ll need custom authorization logic.

What are you building?

This is the decision that cuts through the framework confusion. The agent type determines which layers you invest in and which tools to pick at each one.

A stateless tool caller answers questions from a knowledge base, looks up an order, or checks inventory. You need a provider SDK, MCP, and Postgres. No framework, no vector database. This is a weekend project.

A multistep workflow processes a refund end to end, reviews a PR across five files, or triages and routes support tickets. Steps depend on each other, things fail in the middle, and humans need to approve before the agent acts. You need LangGraph, MCP, and eval. Build evals before you deploy because these agents break silently.

An agent that learns remembers your preferences across sessions, gets better at your codebase over time, or tracks project context across weeks. You need a memory-first architecture, a vector DB, and eval. Orchestration is the easy part. The hard part is deciding what to remember, what gets dropped, and how you stop old context from polluting new answers.

A multi-agent system has agents that delegate to other agents, split a research task across specialists, or run parallel workstreams. You need the full stack. Two agents passing context to each other is already hard to debug. Five is impossible without trace-level evals on every handoff. Build eval infrastructure before you build the second agent.

Coding agents: All 6 layers in action

Coding agents like Cursor, Claude Code, Codex, and Windsurf are the most proven application of the AI agents stack. All six layers, working together.

At the inference layer, these tools serve hundreds of millions of daily requests. Cursor routes between Claude, GPT-4, and its own fine-tuned models depending on the task. At the protocols layer, MCP servers connect to editors, terminals, filesystems, and Git, which is how the agent reads your code and runs commands. The memory layer uses codebase-aware retrieval with reranking. The agent doesn’t read your whole repo. It retrieves the files that matter for this specific edit.

At the framework layer, these are custom orchestration systems with RL loops. Not LangGraph, not a provider SDK. Purpose-built control flow for code generation, review, and iteration. At the eval layer, Cursor retrains its acceptance-rate model every 90 minutes based on whether users accept or reject suggestions. That’s eval running in production, continuously. And at the guardrails layer, sandboxed execution prevents runaway agents. The agent can write code and run it, but inside a container that limits what it can touch.

The AI agent stack cheat sheet

Every layer scored on the three questions from the evaluation framework: How much state do you need to manage? How much vendor lock-in can you tolerate? And how hard is it to go from demo to production?

The bigger picture

Most teams are building like it’s still 2024. They pick LangGraph before they know if they need state. They add a vector database before they’ve outgrown Postgres. They design multi-agent architectures before they’ve shipped one agent that works. The decision flowchart above exists because a tool-calling chatbot and a multi-agent research system share almost no infrastructure. Treat them the same and you’ll overbuild the first and underbuild the second.

The teams that got past this run evals on every deploy, not once a quarter. Their guardrails sit at the tool call layer, not the output layer. Their memory architecture was designed, not inherited from whatever the framework defaulted to. Most teams ship the opposite: no evals, output-only filtering, and a system prompt that grows until the context window chokes. The gap isn’t talent or budget. It’s knowing which layers matter for your specific agent instead of half-building all six.

The stack is going to collapse. Provider SDKs are already absorbing memory, tool calling, and basic eval into a single API. By early 2027, most teams won’t build each layer separately. They’ll get an increasingly opinionated stack from their model provider and that will be fine for 80% of use cases. The other 20%, agents at scale where the defaults break, will still build custom at every layer. But even then, when something fails in production, you need to know which layer failed. That’s what this article is for.

Sources

1. “The AI Agents Stack,” Letta, November 2024.
2. “Donating the Model Context Protocol and Establishing the Agentic AI Foundation,” Anthropic, December 2025.
3. “120+ Agentic AI Tools Mapped Across 11 Categories [2026],” StackOne, February 2026.
4. Henrik Plate and Darren Meyer, Dependency Management Report, Endor Labs, January 2026.
5. Jason Liu, Context Engineering Series: Building Better Agentic RAG Systems, August 2025.
6. “LangChain and LangGraph Agent Frameworks Reach v1.0 Milestones,” LangChain, October 2025.
7. State of Agent Engineering, LangChain, December 2025.
8. Yunfei Bai, Allie Colin, Kashif Imran, and Winnie Xiong, “Evaluating AI Agents: Real-World Lessons from Building Agentic Systems at Amazon,” Amazon, February 2026.
9. OWASP MCP Top 10, OWASP.