Christina Stathopoulos, the data and AI evangelist behind Dare to Data, continued her run sorting the week’s most impactful stories into a handful of themes we’ve been watching play out over the past month: more firms investing in the compute AI runs on, more concerns about who controls a model’s borders, and more AI-generated code posing challenges to scaling AI enterprise-wide.

Christina also quickly shared two updates from the frontier labs that we won’t get into below. First, OpenAI finished rolling out GPT-5.6, its family of models tuned for different workloads with an option to dial reasoning up or down, and launched ChatGPT Work, an agent workspace that connects the model to Slack, calendars, documents, and other enterprise tools. Anthropic, meanwhile, published research describing a hidden internal workspace it’s calling the “J-space” that suggests that Claude organizes and manipulates ideas before producing a response. It isn’t proof of anything like consciousness, as Christina was quick to note, but it’s one of the clearer steps yet toward inspecting what a model is actually doing between input and output. That kind of visibility is critical for catching problems like deception or unsafe behavior before they show up in an answer.

More AI labs are turning into chip companies

Last week, Christina covered the opening moves in an AI hardware race, with research from IBM and NVIDIA and a joint OpenAI and Broadcom project. Now there’s news that Chinese company DeepSeek is developing its own inference chips to cut its dependence on NVIDIA and Huawei, and Anthropic is in early talks with Samsung to build a custom AI chip. And as we saw with IBM’s sub-1 nanometer tech, chips are getting denser. Researchers in South Korea have developed a manufacturing technique that stacks more than 10 ultrathin memory chips, packing about four times the density of today’s commercial high-bandwidth memory into the same footprint. The layers align within about six micrometers, roughly a tenth the width of a human hair. The short distances between layers mean the signal doesn’t have to travel as far, making the whole stack run faster and more efficiently.

For AI companies, owning more of the stack is a way to control the cost and performance of running models once they’re built. As chip access becomes a lever in trade and security policy, it’s also a way to circumvent obstructions related to a supplier’s roadmap or a rival’s export policy.

A new security threat underscores the broader geopolitical stakes

JADEPUFFER is the first documented ransomware attack in which an AI agent carried out the entire operation end to end. A human chose the target, then the agent took over, exploiting a known vulnerability, searching for passwords and API keys, moving into the production database, encrypting it, and even writing its own ransom note, all without a person directing each step. Security teams have been bracing for this kind of sophisticated AI-driven attacks. JADEPUFFER is likely the first of many.

That growing threat surface was one reason why AI security took up so much of the conversation at the recent NATO summit in Ankara, where leaders discussed how AI is reshaping cyberattacks, drone warfare, disinformation, supply chain risk, and the speed at which leaders are expected to make high-stakes decisions. Paralleling US restrictions on who can access domestic models, China may also be moving to limit overseas access to its own frontier systems, and Alibaba is banning US-made models for its own employees. We’ve been tracking this story since May, when the US government’s on-again, off-again restrictions on Anthropic’s Fable and Mythos models offered an early sign that frontier model access was becoming of national interest. Christina shared findings from Our World in Data that show just how much the market share of Chinese models has grown from a year ago: Per data from OpenRouter, Chinese model usage at US-based companies, measured in tokens, is approaching parity with US model usage. For technical leaders, that’s a reminder that model choice is now as much a supply chain decision as a technical one, and it’s increasingly one with geopolitical repercussions.

Two challenges to watch for as enterprises scale AI

Now that code is effortlessly simple to generate, the real engineering work is making sure that AI-created code is correct, secure, and safe to run in production. As many in the field are now realizing, that’s easier said than done. A recent study of nearly 200,000 pull requests across more than 800 developers found that AI nearly doubled coding productivity, and reviewers couldn’t keep pace. Each reviewer is now responsible for roughly twice as many pull requests as they were in the years before widespread AI adoption, and the share of pull requests getting human review fell from 89% to 68%, with automated reviews filling the gap. It’s part of the same story Matt Palmer told on the show a few weeks ago when he compared running a team of agents to managing a mid-size team of human developers: “You’re just sending messages all the time, and you’re checking in to make sure things are being done,” he explained. The increase in velocity sets up a real risk of cognitive fatigue and burnout.

Here’s another challenge enterprises are facing as they scale AI: They’re connecting more and more of their data, workflows, content, and business processes to a single AI provider. As we already learned in the data space, the more attached you become to that provider, the harder it is to switch down the line. The solution to this vendor lock-in is to build an AI stack and the workflows around it that keep you in control of your data and ensure you can swap models as the technology evolves. Enterprises that treat model choice as a one-time decision are setting up the same dependency problem that OpenAI’s GPT-5.6 and Anthropic’s chip talks are trying to avoid, just one layer up the stack.

What’s next

Christina will return next week with another sweep of AI news, including a first look at Apple’s lawsuit against OpenAI, New York’s pause on new hyperscale data centers, and a landmark ruling in Germany holding Google accountable for misinformation generated by AI Overviews, plus updates on DeepSeek’s IPO plans, OpenAI’s first AI hardware device, and Anthropic’s new enterprise deployment unit. Join her live on the O’Reilly learning platform or catch up after the fact on YouTube, Spotify, Apple, or wherever you get your podcasts.

And if you want to keep learning between episodes, check out our new weekly show Zero to Agent in 30 Minutes, our AI Codecon live event on August 31, and The Agentic Enterprise now in early release on O’Reilly. Christina’s also hosting the AI Superstream on AI harnesses next week on July 23. Hope to see you there for this four-hour deep dive on turning models into agents and running them securely at scale.

I keep seeing the same idea in conversations about agents: Detailed specs are old-world overhead now. Give the model a rough goal, let it explore, fix what comes back, move on. It sounds efficient but it also hides the cost.

A simple prompt looks cheap and tempting because it gets implementation started right away. Then the correction loops start. You review output, clarify intent, ask for changes, rerun tests, find the next gap, and do it again. Someone still has to decide whether the result matches the real goal. That person becomes the oracle.

At the other extreme, full formal specification is obviously expensive up front. Writing acceptance criteria, contract tests, or behavior-driven development (BDD) scenarios takes real effort. But the downstream cost is different because more of the oracle is executable. A test checks the same condition every time. It doesn’t get tired, rushed, or optimistic five minutes before lunch.

That is the actual trade-off. The question is not whether specification is good or bad. It’s where the minimum total cost sits. For most agentic work, it’s somewhere in the middle: enough structure to constrain the work, enough examples to make intent concrete, and enough executable checks that review does not turn into guessing.

Zero spec is not intelligent and lean; it’s just costly vibe-coding.

The bottleneck moved, not disappeared

Software engineering was never mainly about typing or even producing code. It was about deciding what should exist, what should never happen, which trade-offs matter, and what “done” means once the problem touches the real world.

For years, teams discovered missing specification through human friction. A reviewer noticed an edge case, QA found the path nobody described, a senior engineer carried half the real requirements in his head and translated them one meeting at a time. None of that was elegant, but it did force ambiguity into the open.

Agents change that fundamentally. They make implementation much cheaper and much faster. It also means an underspecified idea can turn into a plausible system before anyone has really agreed on what the system is supposed to mean.

In the old world, vague requirements ran into human slowness. In the agent world, vague requirements run into machine speed.

That’s why specification suddenly feels important again. It was always important. We just used implementation cost as a crude forcing function and called the result process.

Writing the spec is not enough

This is the part I see people skip most often. They talk as if the sequence is simple: write the spec, then let the agent implement it. The missing step is the expensive one.

The spec itself needs review.

Even a careful spec can fail in familiar ways. It can contradict itself or cover the happy path and say nothing useful about retries, rate limits, or partial failure. It can describe behavior that sounds precise but cannot actually be verified. And sometimes it is precise in exactly the wrong way: it says what you wrote, not what you meant.

When an agent executes a flawed spec faithfully, the failure gets harder to diagnose. The implementation may look coherent. It may even pass the checks you provided. But the real problem lives upstream, in the spec, so fixing it means unwinding code and reasoning together.

That’s why I think spec validation deserves its own line item. Before implementation starts, someone needs to ask a few plain questions. Is this internally consistent? Is it complete enough for this task? Which parts are testable? Where are we still depending on human judgment? Which failure modes are missing because everyone silently assumed them?

Agents can help here, but only if we use them for something more useful than “write requirements.” That prompt usually produces polished fog. A better prompt is much more specific:

Draft the smallest spec that would let another agent implement this safely. Include assumptions, nongoals, acceptance criteria, edge cases, observable outcomes, and open questions. Mark which claims can become automated tests and which still require human review.

After that, hand the draft to a different agent and tell it to attack the result:

Find contradictions, ambiguous terms, hidden dependencies, untestable claims, missing failure modes, and places where an implementation could pass the written criteria while still violating the intent.

Even that simple workflow lowers the cost of getting to a spec that is worth human judgment.

Why multi-agent systems need stronger contracts

A single agent working on a small, bounded task can often recover from loose instructions. The loop is tight, the blast radius is local, and a human can usually steer it back on course when it drifts. Humans can even easily spot the drift to begin with.

Multi-agent systems are a very different problem. Once one agent’s output becomes another agent’s input, interpretive drift starts to compound. Agent B does not know Agent A misunderstood a requirement by 10%. It just treats the output as ground truth and keeps going. By the time a human sees the result, the original mistake may be buried under several layers of competent-looking work.

At that point, the spec is no longer just guidance but more like a contract.

That contract needs more than a paragraph of intent. It needs schemas, invariants, allowed ambiguity, validation rules, and explicit failure behavior. In many cases, it also needs contract tests, typed interfaces, and machine-checkable handoff formats. The handoff is part of the product, which is less glamorous than people hoped, but much closer to reality.

This is also where BDD and executable acceptance tests belong. Their value is not just the methodology, it’s that they move part of the human oracle into something repeatable. When behavior is stable enough to specify precisely, an executable spec is often cheaper than another round of review.

A spec should have an expiration date

There is another failure that teams make here: It shows up when they keep pushing on the specification curve as if more text is always safer. It is not. At least for current models it’s not.

Chroma’s work on context rot makes the first part of the problem clear: Model performance gets less reliable as the input grows, even on simple tasks. In coding projects there is a second problem on top of that. The more design prose, examples, plans, comments, tickets, and old acceptance criteria you stuff into the context, the less obvious it becomes which parts are instructions and which parts are artifacts.

I wouldn’t call this prompt injection in the security sense. Nobody is trying to attack the model. It’s closer to self-inflicted instruction drift. The context contains old design intent, current implementation, half-valid examples, generated plans from three sessions ago, and maybe a stale software design document that still describes classes that no longer exist. At that point, the model is not reading one spec, it’s averaging across competing sources of truth.

That’s when overspecification stops helping and starts confusing the model. The agent can no longer tell whether a paragraph is an active requirement, a historical note, or something the code has already replaced.

A design document is useful early because the code doesn’t exist yet. Later, it needs to shrink. Once interfaces, tests, and invariants are real, the detailed build plan should start disappearing. “Keep the parts” code is bad at expressing on its own: business rationale, non-goals, safety constraints, external contracts, and the few invariants you do not want rediscovered by trial and error. Delete the prose that just restates what classes and methods already do.

Otherwise, you end up with two specs. Humans will complain about that in review. Agents will often try to obey both.

APIs can make code behave like spec

There is also a more optimistic version of this story. Some codebases reach the “code is the spec” point faster than others, and API design is a big reason why.

If an internal API hides behavior behind conventions, weakly typed parameters, setup magic, and generic errors, an agent cannot treat the code as the spec. It has to reconstruct the rules from scattered prose and trial and error. That’s slow for humans and worse for models.

The opposite is also true. An API with explicit names, task-level methods, strong types, readable validation, useful examples, and actionable errors gives the agent something concrete to stand on. If the agent can inspect the surface area, see what a method does, understand what input is legal, and recover from errors without guessing, then the code carries much more of the specification load by itself.

This is where the AI-friendly API design ideas matter in practice. Explicit discoverability beats convention. Methods should line up with real tasks instead of forcing the agent through a dozen fragile steps. Types and validation should show what legal input looks like. Error messages should point to the next fix, not just announce failure. Introspection and examples help the model learn the shape of the API from the codebase it already has. Performance transparency matters too, because an agent will happily write a correct and terrible loop around an expensive call if the API gives it no clue.

This isn’t only about public SDKs. It applies to internal service boundaries, library clients, repository abstractions, and even the helper classes in a large monorepo. The easier an API is to discover and inspect, the easier it is for an agent to treat the code as the authoritative spec instead of dragging more prose into the context. I’ve written about all this before in more depth if you’re interested.

Where to invest

What I strongly believe is that there is no single right amount of specification. The answer depends on the kind of work you’re doing. For a small, well-bounded task, the sweet spot is usually structured intent: the goal, a few examples, nongoals, and clear acceptance criteria. That is often enough to keep the agent productive without making setup heavier than the task.

For deterministic work such as CRUD flows, API integrations, and data transformations, the optimum moves to the right. These domains are easy to constrain and easy to test. More specification pays for itself quickly because it cuts repeated review and rework. This is where BDD, contract tests, and executable acceptance criteria help most.

For exploratory work such as architecture options, research synthesis, or novel product ideas, the optimum moves left again. Over-specification can kill the very flexibility that makes the agent useful. In that case, I would rather specify boundaries than outcomes: what must be true, what must not happen, what evidence is required, and which decisions still need a human.

For multi-agent pipelines, the optimum moves right once more. Every boundary between agents needs a contract. Without that, you aren’t coordinating a system. You’re stacking interpretations and hoping they cancel out.

The common rule across all four cases is simple: Validate the spec before you scale the implementation.

What survives from Agile and XP

I do not think agents make Agile or XP irrelevant. They make the useful parts easier to separate from the parts people were already tolerating.

The first casualty is the ceremony that existed mostly to coordinate human effort hour by hour. Daily status meetings, inflated backlog rituals, and estimates presented with more confidence than information do not get stronger because an agent wrote the code. If anything, they get weaker. Agents can change the shape of a task so quickly that old effort estimates become fiction even faster than before. That doesn’t mean planning disappears. It means planning has to stop pretending it can predict implementation cost with the same comfort it had when code was the slow part.

What survives from Agile is the feedback logic. Short cycles still matter. Thin vertical slices still matter. Customer or stakeholder review still matters. Working software is still better than progress theater because agents can generate a lot of convincing wrongness very quickly. In fact, I would argue that fast feedback matters more now, not less. If a team can go from vague idea to large implementation in a morning, it also needs a way to discover by lunchtime that the idea was wrong.

XP survives even better because it was always about keeping learning close to the code. Test-first thinking still matters because executable checks get more valuable as implementation gets cheaper. Continuous integration still matters because every agent change needs a gate. Refactoring still matters because agents can happily produce code that works, passes a few tests, and still leaves you with a structure nobody wants to maintain next month. The machine has no pride here. It will generate a mess with perfect confidence.

Pair programming changes shape, but the core idea survives. I still want design judgment close to code generation. Sometimes that looks like a human working directly with one coding agent. Sometimes it looks like one model generating code while another model reviews it with a narrower brief. Either way, the useful part of pairing was never two keyboards in harmony next to each other over a coffee with their humans. It was fast design feedback before the code settled into place.

Small releases also survive, maybe for a less romantic reason. When agents can make very large changes cheaply, the temptation is to accept very large diffs cheaply too. That is a bad idea. Review, rollback, and diagnosis are easier done in small batches. A short-lived feature branch is easier to reason about than a 4,000-line monster.

What fades is methodology as reassurance. What survives is methodology as error detection. Agile and XP were at their best when they made it cheaper to discover that the team understood the problem badly. That’s still the job. The agent era just removes a few excuses and adds new ways to be wrong at high speed.

The real leverage

The promise of agentic development is real. Agents can make implementation dramatically cheaper, but once code gets cheap, specification and verification become the place where projects succeed or fail.

The teams that get the most leverage will not be the teams that specify the least. They’ll be the teams that know when three bullets are enough, when they need a real contract, and when the contract has to become executable.

The agents are getting better. The decisions are still ours.

The tech industry is measuring AI productivity all wrong, and Mozilla MLOps engineer and University of Chicago instructor Chelsea Troy makes a strong case for why. The real opportunity, she argues, isn’t shipping more code faster but finally having the bandwidth to run the experiments, tests, and simulations that engineering teams have always wanted to run but never had time for. Chelsea joined Ben to cover the state of entry-level hiring, why the software engineering interview has been broken for decades, what it means to teach Python in 2026, and why token efficiency should replace token consumption as the industry’s dominant productivity metric.

About the Generative AI in the Real World podcast: In 2023, ChatGPT put AI on everyone’s agenda. In 2026, the challenge will be turning those agendas into reality. In Generative AI in the Real World, Ben Lorica interviews leaders who are building with AI. Learn from their experience to help put AI to work in your enterprise.

Check out other episodes of this podcast on the O’Reilly learning platform or follow us on YouTube, Spotify, Apple, or wherever you get your podcasts.

Transcript

This transcript was created with the help of AI and has been lightly edited for clarity.

00.31
Ben Lorica: All right. So today we have Chelsea Troy. She’s part of the machine learning operations team at Mozilla. And she’s also developing a bunch of courses for O’Reilly around agentic coding skills. Chelsea, welcome to the podcast.

00.47
Chelsea Troy: Thank you for having me.

00.49
All right. So two things that pop out there: agentic coding and skills. So first of all, agentic coding. Chelsea, so you personally, to what extent are you using any of these agentic coding tools.

01.06
Sure. So I think that. . . I have sort of a number of different jobs that I do. I work, as you mentioned, as a machine learning operations engineer at Mozilla, where I help machine learning engineering teams get their work to production. And then I also teach at the University of Chicago, and I teach a machine learning class within the set of courses that I teach, in addition to some of the stuff at O’Reilly.

So in all three of those areas, I find myself needing some expertise in agentic coding, not, like even in addition to specifically whatever I might be doing with it, because a lot of my colleagues or my students are using it, and it’s important for me to understand how it works, because I need to be able to advise on that, and I need to be able to assist with that.

01.55
So right now, for example, at Mozilla, we are exploring the extent to which agentic coding suits our values, to which, the extent to which agentic coding suits our, like, workflow, the kinds of things that we are trying to do, particularly internally. But, actually the places where I’ve seen it most in the places in which I have found myself needing to develop the most nuanced takes on agentic coding come from the work that I’m doing with my students, because I have these students, the graduate students in computer science, and they are trying to figure out how to navigate early career software engineer type of roles.

How are they going to apply to them? How are they going to be evaluated for them? How are they going to succeed at them? How are they going to be promoted out of those roles? And I think that they have a lot of questions about those things that are coming to me. They want to know the answers to these questions, and these are not questions that I naturally have experience to answer, because at this point, I’ve been a software engineer for the better part of two decades.

The last time that I applied for a role was many years ago. The last time that I applied for an entry level role, things were so drastically different than what these students are experiencing now. And so I find myself doing a lot of my research, a lot of my implementation, a lot of my experimentation towards this end of understanding how this is going to work for them, how can students expect to learn now? What are students going to be expected to know? What our entry level engineer is going to be expected to know? What are companies expecting of entry level engineers now, and what is it going to mean for them to have people advance in skills as these tools are available and with the expectation that these tools are going to be available for students. So, a lot of what I do is around figuring out how to answer those questions right now.

03.57
All right. I have lots of questions before, but before I do that, a quick shout out to the University of Chicago, where I have friends on the faculty, Mike Franklin and Bob Grossman in particular. All right. So I assume, Chelsea, that, the difference between the people graduating this year, 2026, and the people who graduated last year, 2025, as far as interesting expectations around agentic coding tools, there’s a big difference, right?

04.30
I think so, and I think that part of that is that over the past year, we’ve seen a great deal of development in these products specifically for programming uses. And I would say that my specialization within the use of these tools is pretty much exclusively their use on programming and then data visualization projects. I would say that outside of that, my expertise peters off very quickly, but I’ve spent a lot of time on the intersection of these tools and learning on these tools and completing the tasks that people are expected to complete inside of a workplace, and what that means inside of the more holistic view of what needs to get done on a team.

But I would say that in 2025, students still. . .and this is a verification and sort of their cycle of work is still very important for them to maintain a very firm handle on. But in terms of the results that they’re able to get from using an agentic tool, for example, on completion of a project they might be doing for their academic degree, they’re having a lot more success now than they were a year ago, which raises, interesting questions about what they need to be doing by hand, whether we can verify that they’re doing it by hand. But I think also more broadly and perhaps more importantly, like what do they need to be keeping in mind while using these tools? What are the values for them to take forward as they’re using these tools? And what skills are important for them to make sure that they’re developing? And to what extent can we support them in building those skills and verify that they’re building those skills?

06.02
So I am assuming the class you taught in 2025 is very different from the class you taught in 2026, which might be also very different from the class you’ll be teaching in 2027.

06.14
It’s possible for sure. And part of that is because some of the classes that I taught this past year, I taught applied data analysis, which is a machine learning and data analysis class, that we’re changing the name of to, I want to say applied statistical learning next year. But this past year was the first time that I taught it.

However, in years prior to that, I had taught intermediate Python several times. This is an accelerated version of the Python programming class, and it’s one that I have taught in the fall for a couple of years running, but I ended up completely redesigning this class the last time that I taught it, and the reason that I ended up completely redesigning it was that the previous curriculum for this class focused heavily on the syntax, what syntax people need to know, what that syntax does in Python, and how to remember what that syntax does, the difference between the different syntaxes. And the thing about programming languages in general, in Python in particular, is that they play very well with these types of agentic coding tools. And part of the reason for that is that the way that a large language model is built is by training on the patterns in text, and the patterns in programming text are remarkably strong relative to the patterns in natural language.

We have a much smaller set of tokens that are used in programming relative to natural language. We don’t really have things like pronouns and referential verbs, or referential nouns inside of programming. If you want to refer to a variable, you refer to the variable by its exact name, with the possible exception of like self or something like that. 

07.51
And so we have much stronger patterns. We have much stronger patterns as to the order in which these tokens are used. And so these tools have a lot of success from a relatively small number of patterns of programming language, but particularly Python, which has an especially small set of tokens and an especially strong pattern as to how it’s built, it can look at a relatively small number of examples and deliver valid outputs and valid output for whatever it is the problem is that you are having and to the extent that you’ve been able to describe that problem precisely, LLMs have a lot of success at generating valid Python, which begets the question, what is it important now for a Python programmer to know if they have these automated solutions available for generating Python? And so when I redesigned the class, I refocused it less on the syntax and more on the why.

Why is Python implemented the way it is? How is the Python implementation different from other programming language implementations? I think an idea that students do not have as much exposure to as I think might be useful is that different programming languages exist for a reason. They have different philosophies as to how an interpreter should work. There are choices to be made. There are trade-offs to be navigated in the design of a programming language, such that different answers exist that result in different programming languages being appropriate for different tasks. This is particularly a revolution for students who have done most or all of their programming in Python without being told necessarily why that is. And of course, part of the reason that that is, is that Python is a relatively useful. . . It generalizes fairly well to the type of problems that we’re teaching students to solve.

And it also has, because of a relatively small number of tokens, a relatively friendly learning curve for students. And so now the class focuses on why Python for which tasks, what were the trade-offs that people navigated and why. 

09.52
The other thing that the class now focuses on is what we can learn from Python about the growth and maintenance of a code base. Because there are relatively few code bases in the world that match Python’s degree of complexity and the number of users that Python has, but also the amount of openness with which it has been developed. There are reams of documentation on every code change. There is publicly available discussion on all of the code changes that have been made to the Python interpreter, as well as detailed documentation on the alternatives that were considered and passed up in favor of the way that Python works now.

And so all of that documentation makes Python a really useful case study for how you might work on such a massively impactful programming project yourself in the future, whether or not it’s in Python, because Python provides us with sort of like, a gold standard for how a complex project with a large user base might be maintained over time.

10.51
So in your work at Mozilla, I’m assuming you interview a wide-range of potential engineers, from the entry level to the more senior. So what kinds of tips are you giving your students in terms of. . . What is the change in the interview process in light of the agenda and coding tools? Because before they would give you all these little coding assignments, right?

For example, I work with startups where they even encourage some of the candidates to spend a day or two days at the company. And here, here, maybe you can try out this little project and then at the end of the day, well, we can discuss it. So what is the change, Chelsea, in terms of the interview process?

11.48
Yeah. So it’s an interesting question because I think that interview processes in programming have in some ways codified a difference between how we evaluate developers and how developers provide value to an organization for a pretty long time. Hillel Wayne has this really excellent series about the history of software engineering interviews, and the fact that many of our most common interview questions—and this is before the advent of agentic coding—many of our most common interview questions or interview questions we inherited over time from a period in which programmers had to do a lot more from scratch.

So, for example, we would ask interview candidates to implement a linked list from scratch. And if you were to ask a programmer in 2005 why we ask them to implement a linked list from scratch, the reason that we would give is that we want to evaluate their critical thinking capability and their architectural design capability and all of these things.

But that’s actually a retcon answer as to why we would ask that interview question. The reason we ask that interview question is that we inherited it over time, from an interview process that happened decades ago. And in that interview process, the reason that we asked developers to draw up a linked list from scratch is that, in fact, we did not have high-level programming languages that provided you with a linked list. And so in order to be able to do your work, you needed to be able to make a linked list. We got that question not because it’s some sort of theoretical critical thinking question but because at the time that it was developed, it was a very pragmatic question that related directly to the job that people were supposed to be doing.

13.37
And as programming languages developed, that question was no longer really pragmatic in the sense that it wasn’t a thing that developers were going to need to be able to do on the job anymore. But because we had lost touch with the reason that we asked that question, because we had lost touch with the developers of that question, because the programming industry had changed so much in the intervening period, and also because of a sort of a selection bias associated with who evaluates interview questions—anybody who’s in a position to evaluate an interview question is a person who passed that interview question because they work here—the question never changed. The why got lost. So we came up with this new why that didn’t quite fit the question.

And I think that for a long time we operated without the why. As to our interview processes in programming, famously there was this book, of course, Cracking the Coding Interview, which was theoretically about how to do how to succeed at coding interviews as a candidate, and after Cracking the Coding Interview came out, many companies started using Cracking the Coding Interview as a model of what they imagined Google did in the interview process, which therefore meant that was what they should do in the interview process, because Google was such an exciting place to work.

And so this book had these follow-on effects. I think that, to be honest, a lot of the programming industry has been kind of thrashing around on how to conduct an interview appropriately for a pretty long time. And I think that that continues as the tools that are available to our engineers evolve, while our interview process continues to be kind of this sort of decentralized thrashing as to what it is that we need to do.

15.21
And so I think the question of how the interview process is evolving, it ends up being highly variable from company to company. I think that some companies are changing relatively quickly. Some companies are changing more slowly. Some companies are embracing the use of AI in the completion of interview questions, and some companies are asking that they are able to continue to evaluate based skills and looking for ways to attempt to evaluate based skills, which of course means verifying that folks are not using this tool in the interview, if that’s the thing that they want to do.

And so from company to company, I find that it’s different, which makes it challenging to instruct students on how to address this. But I find myself thinking about this question from two angles. One of them is as a designer of interviews, I’ve designed some of the programming interviews that Mozilla uses for my team, and the other is as an advisor of students who might be taking these interviews.

Those angles are a little bit different because, on my team, currently the lowest position for which I have designed an interview has been what we call IC3. This is a senior software engineer. So I’ve designed for senior, I’ve designed for staff, and then I’ve designed for senior staff as well. So those are IC3, 4, or 5.

And in those roles, it is already supposed to be important that developers are able to evaluate trade-offs at the strategic architectural level for a codebase. And so in those interviews—we do them live; we don’t do a take home—I am working with developers to understand how they are going to navigate trade-offs in the design of a system, and we may ask them to write a line of code here or there.

We may ask them to write a function, but are largely asking them to walk us through their process. And it’s not the lines of code that are important. I have not found this interview style to need to change very much from the past, because it is so much a part of a conversation, and I think that that is still valuable and relevant to the work that we end up using.

17.22
A long, long time ago, when I was a junior engineer, I interviewed at Pivotal Labs and Pivotal Labs’ interview at the time was, I don’t know if this is still true, but at the time it was relatively famous for being the same entry-level tech, or rather the same sort of tech interview as you were entering the company for everyone. It was called the RPI, which stood for Rob’s programming interview, referring to Rob Mee, who was one of the founders of the company. And what it was was it was asking you to build. . . You could find it all over the internet. Technically, we’re not supposed to talk about what was in the interview, but if you want to go look, you can find it on the internet.

But we were asked to build a specific thing. We were asked to do it in Java. However, we were not the interview candidates writing the code. The interviewer was responsible for typing in the code and the interviewee was responsible for communicating the idea of what needed to happen sufficiently precisely, that the interviewer would then be able to implement that towards the goal that we had. And I think about that interview a lot, because I’m not going to say that interview was ahead of its time. I don’t think it was predicting that something like a. . .

18.40
Prompt engineering.

18.42
Right, but it was indeed this. Programming language aside, a part of the reason that the interviewer was the one typing the code was that we wanted to be able to interview folks coming from any language, but we were going to do the interview in Java because at Pivotal, the thing that you did was that you were working as a consultant on different projects.

It was theoretically possible for you to get staffed on a project in a language you didn’t know, and you were expected to be consulting level on it within three weeks, which meant you need to be able to learn programming languages fast, but the expertise that we’re selling people is precisely this thing your judgment: your ability to articulate what needs to happen in a system regardless of the programming language.

19.21
And I do think that that skill set remains the one that is the most important, both for companies to interview on and for interview candidates to be able to produce. You know, some companies still do this thing where they’ll put you on a video call and they’ll ask you to write down Dijkstra’s in 40 minutes. And theoretically it is a critical thinking challenge.

And where I land on this is that ultimately, that interview is a validation that you have already been taught Dijkstra’s algorithm because Dijkstra did not come up with Dijkstra’s in 40 minutes. So this is not some general critical thinking thing; it’s a memorization question effectively. For a memorization question, I don’t know that I have an opinion on like whether or not you should actually validate that people memorized it versus determined that they’re not, I don’t know, using an LLM to pretend that they memorized it or whatever, because I don’t think that this type of tech screen, asterisk is particularly useful.

20.24
Anyway, I think a much more useful tech screen is one that evaluates people’s decision-making. And I think that to the extent that LLMs have forced the interview process to move towards actually evaluating decision-making, that might be a good thing for tech interviewing overall. And I think it could be a good thing for junior developers as well, because it focuses—to the extent that junior developers are able to pick up on that—entry level developers are then developing that skill set that’s much closer to what’s actually important on the job than whether you’ve memorized Dijkstra’s, which you’re never going to have to code from scratch yourself.

21.04
Have you noticed, Chelsea, among your students who are on the job market. . . So this year in the job market, compared to on the job market last year, has it been more challenging to get this first or this entry level or first job for these students year to year?

21.29
I think that it is really challenging right now. I don’t envy students who are trying to go into industry at the moment. And I think that actually is. . . LLMs play a part in that. I think the biggest parts that LLMs play in that is that companies are experiencing a lot of turmoil figuring out, first of all, how to evaluate entry-level candidates.

And also, there’s all this consternation about whether companies need entry-level candidates. There’s this idea that, maybe if we just have senior engineers, they can delegate to agentic coding tools, and then we don’t need to hire entry level engineers. I think companies are going to be able to kind of try that for a few years. And I think then eventually it’s going to become clear that continuing to invest in talent for the industry is going to be an important thing for companies to do, regardless of the tools that are or are not available.

But I think we are still currently in this few-year phase where companies are experimenting with whether we can eliminate this entire class of employees. I think ultimately the conclusion is going to be we cannot. But because we are in that period, I think that currently there’s a lot of anxiety among students about whether there’s going to be availability of roles.

22.57
And also it has been the case for a long time that students feel like they have a hard time getting that first role. I remember 15 years ago being very, very concerned about like, oh, once I get blah level of experience, I know I’m going to have my pick of jobs, but until I get that much experience is going to be really challenging and I needed to go the extra mile a fair amount back then as well. . .and, you know, build relationships with hiring managers, build relationships with other engineers, understand what it was going to be like at various organizations.

I think a lot of students try cold-emailing like 100 companies or sending their résumé to 100 separate companies, and that doesn’t work. And then they feel like things are very hard and they are—things are really hard right now. But I would say that a lot of the challenges associated with getting hired now are similar in shape to challenges of getting hired from before that, you know, [are] much more intense right now.

24.00
Yeah. Yeah. The other thing that it seems like, Chelsea, companies are doing. . . So there’s the notion of “Maybe we should slow down hiring entry-level.” That’s one of the mistakes they’re making. The other thing that seems to be fashionable these days is, “Hey, actually, we should have all these managers code again, right?” Because basically now that there’s these coding tools, we don’t need these managers.

24.29
I think there’s. . .

24.30
Am I just imagining this? Because I’ve had these conversations with a bunch of people. It seems like it’s a real thing.

24.39
You know, it may be the case. I don’t think I’ve had as many conversations with folks in environments where managers were compelled to code. I do know that in my own personal experience, I’ve talked to a number of managers who are very excited about the way that agentic coding tools now give them the ability to write code with. . . A lot of times, it’s like a bandwidth issue. They have limited time; they have other responsibilities. Or sometimes it’s this like, “Well, I became a manager six years ago, and because the pace of technology moves very fast, that means that my skills are now obsolete. And so I no longer have the ability to actually keep my hand on the wheel as to what we’re doing. But now with agentic tools, I don’t necessarily need that same level of update, because I still have the ability to precisely communicate my requirements,” is the idea, “and if I can precisely communicate my requirements then agentic tools can do it for me.” I think a lot is still up in the air as to how useful this is going to be.

25.35
I know that a number of larger companies that pivoted towards attempting to siphon more work into LLM tools are now backing out and looking at taking a more holistic view as to how that’s going to work. So from a larger industry perspective, I think I still have a lot of questions about where that’s going to go. Is it going to be successful? Are people going to like it? What’s going to be the impact on the products themselves?

But I think that in my kind of personal sphere, I’ve talked to a number of managers who have been really excited about the possibilities that these tools provide for giving them the entree back into some level of individual contribution.

26.22
And I think that there is a lot of value for us to derive from that excitement in terms of understanding, like what managers missed about individual contribution previously and what we can learn about role development from that. I think that it’s been the case in the tech industry for a long time that we kind of make fun of the fact that you write code, you’re a good technologist, you do your things, you create value.

And to the extent that you are successful at it, you get rewarded with a promotion to a job that uses none of the skills that you just developed, and a whole bunch of skills that you now don’t have with, depending on the employer, widely differing levels of support on developing the completely new skill set that you’re now going to need as a manager.

And I wonder whether there is light to be shed by the advent of these tools. On and on and on, the possibilities for alternatives to that strategy where somebody coming from individual contribution has the ability to continue an individual contribution while also helping to grow teams. 

27.38
There is a developer who back in the Twitter days I used to follow, his name is Marco Rogers. His handle was Polotek, and he would talk about career development as a person who, if I recall correctly, started as an IC, became a manager, and then crafted a career path for himself in which he bounced back and forth between individual contribution and leadership roles and found that that worked really well for him, or posited that that could work really well, particularly juxtaposed against the sort of traditional career path that we talk about where if you become a good-enough developer, then you become a manager, and now you’re exclusively in the managerial track, despite the fact that your interest, your skill, and in a lot of cases for many of these people, your passion lay in the building of things. And now there is an argument to be made that you’re still building things, but you’re building as a team, you’re building a community, all of these things. 

But if we take that sort of like metaphor out of it for a moment, a lot of times these folks in leadership deeply miss this piece of the craft that they’ve lost access to. And this tool creates sort of a detour that allows them to express that interest in the craft again, which I think gives us license to examine whether they should have been separated from the craft in the first place, whether that was the appropriate way to develop the standard career path in software engineering.

29.02
I like that. I like that bouncing back and forth because I think that I’ve actually had a lot of friends who’ve done that as well. And if anything, I think the misunderstanding of these agentic coding tools probably is much more in the senior leadership role rather than the middle management role.

I’ve actually just tried to compile a bunch of studies. Because, on the one hand, you have these developer surveys, and obviously developers always have a tendency of overestimating things. And then there’s the actual telemetry. It turns out there’s this kind of an attenuation. So this intensity funnel where, you know, developers might be writing a lot of code now with these tools, but the number of software shipped actually hasn’t grown as much.

And then if you go all the way down to the end to the app stores—so Apple App Store, Google Play, and all these places—the actual number of. . . This usage of software hasn’t actually moved the needle. The tools haven’t moved the needle as much, just as much as the fact that, let’s say, a single developer might be writing 3x more code, right? But if you follow the trail all the way down, it hasn’t actually moved the needle.

And I think part of it is, we all probably feel productive in the sense that if it’s a one-off thing, yes, these tools can make me super productive. I’m never going to use this code again. I’m just going to use one of these tools. But if something gets more serious, then it turns out that it doesn’t move the needle as much because people obviously still have to follow all the rigorous processes. I don’t know what you think.

30.53
Yeah, I think that with regard to the way that these tools are used at the organizational level and the outcomes that we’re seeing, if I were to offer a half-baked, perhaps cancellable take on the situation, I’m a little trepidatious and saddened that a lot of the zeitgeist around the way to use these tools for productivity, theoretically, productivity gains is this idea that what we need is for developers. . . Like the proof of productivity is going to be the developers are closing more tickets; developers are shipping more code; developers are getting through things faster. I think that that focus demonstrates, possibly, a lack of vision as to what these tools could provide for us, because I’ve now been on the ground as an engineer for a while.

31.50
And the biggest problems that we run into are there are many. And of course, there’s always been that there’s not enough hours in the day. We can’t hire enough developers. But truly, that’s usually not actually the main problem that teams have had, in my experience over the last many years. Instead, the things that come up the most often are “We were evaluating trade-offs, and we selected this implementation because we only have the bandwidth for one, and we think this one is going to be the right choice. And we don’t have the opportunity to implement all of the others and experiment. And then based on real experiments, use the implementation that is working the best. So we take a guess or there will be like, you know, we would have liked to do comprehensive testing on that, but we just didn’t have the bandwidth to do the comprehensive testing on that. And so we’re making a guess.”

There’s a lot of developer estimates being baked into the systems that we’ve built because we don’t have the bandwidth to actually run all of the experiments that we might like to run. We don’t have the ability to include all of the rigor that we might like to include. And as you referenced earlier, developer estimates have the level of accuracy that they have, which is, you know, known largely in industry to be not perfect, right?

33.21
I am much less interested in what it means for a developer to ship three times as much code. I’m much less interested in that than I am in what it would mean for a developer to be able to use three times as much code to arrive at the ultimate solution, which might be approximately the same volume as the solution would have been before, or ideally, perhaps even lower volume than the solution before.

Because instead of needing to hedge against all of these possibilities and make an estimate and maybe even, maybe even overengineer preemptively based on all of these different possibilities, we have the ability to instead actually run the simulations, actually try the alternatives against each other, actually run tests, and arrive at this theoretical better solution. That we always knew we were making a guess at, that we felt forced to make a guess at because of our bandwidth limitations.

34.24
I run into this in data visualization as well. You know, we have all of these tools that have been available for a long time to theoretically help us visualize data and create dashboards, because executives want dashboards, and developers don’t have the ability to make custom dashboards all the time. So we have Looker for this, and we have Redash for this, and we have all of these various dashboarding tools that are available.

But the thing about those tools is that they have a limited number of things they can give you. They can give you a bar chart; they give you a pie chart; they give you these various other things. And you compare this to books written by folks who are professionally like artistic data visualizers, right? And they have all of these other options available.

And when we talk about the availability of AI and automation for the purpose of automating dashboards, what we talk about is making more and more customized dashboards with the same bar charts and pie charts and stuff that we’ve been writing before. And the the way that the zeitgeist focus is on the increase in volume that AI makes available I think disappoints me because the availability of this tool removes all of these bandwidth limitations that previously prevented us from being able to doggedly pursue the best quality of the thing that it is that we’re trying to ship. I think our focus on volume as a stand-in for productivity hamstrings us in our ability to actually improve our engineering product with these tools.

35.59
Yeah. I like what you said there. So it seems like then, Chelsea, companies that put themselves in a position where they can actually run these experiments and track the results. . . In other words, I don’t know what the equivalent of an experiment platform. . . You have a staging platform of some kind where you can test out all these ideas. It seems like that’s the right investment to make, right?

So in terms of a company wanting to be able to really leverage these tools, it’s being able to try out all the things that you wish you could try, applying the same rigor you used to apply to only one try. You can now try the equivalent of almost hyperparameter tuning in machine learning. So now if you put yourself in the position where you have this platform where you can try all sorts of ideas, maybe that’s the right investment.

37.05
I think so. I think that there is a lot of opportunity in having the ability to do these things. The thing that I’ve been experimenting the most with lately is data visualization. And I do this for a number of reasons. I work on data visualization, of course, in my day job, because we talk about how to provide dashboards to machine learning engineers to help them understand how their models are performing.

And we also talk a fair amount within the data science team, as you can imagine, on how to present analytics in ways that allow leaders to make business decisions based on the data that we have. So there’s that aspect of it, but there’s also this element of it associated with teaching students. And, you know, I talk to them about a lot of relatively complex concepts, how different models train and things like that. And a lot of times the way that we represent those concepts is with writing or formulae. And one of the things that I’ve been working on is how to represent these concepts for them graphically in a way that helps them understand. And the majority of my experience as a software engineer has been chiefly in backend engineering and a little bit of mobile engineering, but I have not done an enormous amount of frontend engineering.

I certainly have not done enough frontend engineering to have the kind of HTML and CSS skills that it would require for me to hand-code in an afternoon a tree ring diagram that represents the evolution of data science concepts over time, or something like that. That’s a thing that if I wanted to do it, I could do it.

38.40
But like I need to devote a fair amount of my summer to figuring out how I’m going to go about doing that. Meanwhile, HTML and CSS are both text-based mediums for generating images, which means that it is possible to use a large language model to develop at least a baseline on that. And then once I have that, figure out how to tune it using what HTML and CSS are both legible, at least legible to me, in a way that SVGs are not as much.

And so I’ve been largely using HTML and CSS for this. But what they do is there, or what the what the tool has done for me, is it is opened up this possibility for finding ways to represent information in ways that inspire my students and lead them to ask questions, as opposed to intimidating my students and leading them to retreat further back into the tools, because they are afraid that they are not going to be able to implement what they need to implement without them. Rather than pushing them in that direction, I’m trying to pull them forward into a curiosity about the internal mechanisms that I am attempting to explain to them, and I find these tools to be useful to me in providing a layer of text-to-image translation that gives me the ability, to the extent that I’m able, to precisely describe what it is that I want, to build those visualizations.

Which is not to say that it’s a quick process. It’s not a quick process at all. There’s a lot of tweaking, figuring out how the data should be organized, understanding why the data is organized, how it is recognizing all of these discrepancies that then pop up the minute you do this, that aren’t widely understood because we haven’t done this a whole bunch before. But there has been a very real increase in my ability to experiment with visualizations for teaching, because the text to visualization pipeline is streamlined for me by these tools.

40.43
All right. So in closing, I’ll have you predict, which I’m sure is going to be difficult to do given that these things change every week. So in one year’s time and in two years’ time, how does the day of a typical developer or software engineer change?

41.03
Oh, that’s an excellent question. But I think. . .

41.08
One year first and then be more speculative in the two years.

41.12
Sure. As I think about answering this question, I’m thinking back to how the experiences of engineers have changed over the period of other major technical advancements in our field. I think certainly if I were to predict over the next year, I think that engineers’ dependence on these tools will increase.

I think we saw the same thing with the advent of the search engine. Developers existed before the search engine; developers existed after the search engine. The search engine did not take away developers’ jobs by any stretch of the imagination. However, I worked at companies in 2015, where if the internet went down, we all went and played ping-pong because it was generally accepted that if we couldn’t Google stuff, we couldn’t do our jobs.

Nobody would have thought to go play ping-pong if the internet went down in 1985, because largely programmers did not have general access to the internet in 1985. And so I think that dependence on these tools will increase. We’re already seeing folks when the tools go down so they can’t get their jobs done, etc., etc. I think that kind of thing will become. . .

42.20
Or if they’re on the flight and the Wi-Fi is spotty.

42.23
Well, right. There’s this sort of like, yeah, I think that there will be adjudication around the dependence on these tools that is acceptable for developers to have and also acceptable for developers to communicate at the two-year mark. . .

42.40
You know what I will tell you at the two-year mark, here’s what I think/hope will happen—giant error bars around us. Right now, we’re using as a metric tokens consumed for developers. And I think that number of tokens consumed and leaderboards on number of tokens consumed are going to become less attractive for developers to top as subsidies within sort of the LLM industry start to end, and it becomes way more expensive to use tokens.

I am hopeful, in fact, that our focus pivots hard from token usage as a metric for productivity to token efficiency as a metric for skill at using these tools. I am hopeful that that will happen. I am also hopeful that at the two-year mark, we’re well on our way to seeing folks focus on using these tools in some of the ways that you and I have talked about earlier in this conversation, not just as a way to get through tickets faster but as a way to arrive at each ticket and an end that is much more rigorously researched and constructed.

Because the things that we used to just guess at because we didn’t have time to code them ourselves are now things we no longer have to guess at because we don’t have to code them ourselves. And so we develop and start to normalize a practice of actually having tried a few things and arrived at a best solution based on outcomes based on data, rather than making a guess. And then including that in our report as to why we arrived at the conclusion we did, and why the pull request we’ve submitted is the one that it is.

44.27
And with that, thank you, Chelsea.

AI has taken software development by storm. Between the two of us, we build products for software engineers and consumer products for millions of everyday users, so we have skin in the game. We want the AI productivity story to be true. More output, tighter timelines, happier and more productive engineers. Who wouldn’t?

But when we look at the actual research and then look at what’s happening in the real world, we can’t make them agree. Or rather we can, but only if we’re willing to admit that “productive” doesn’t mean what most of the recent discourse thinks it means.

The most uncomfortable finding first

In early 2025, a research organization, METR, ran a controlled experiment with open source developers. They found that (in contrast of what the industry was expecting) engineers using AI tools took 19% longer than those working without them, with a confidence interval of +2% to +39%. The slowdown was statistically robust. This was a different time in the industry. Claude hadn’t released its Opus models, the industry was figuring out what AI can and can’t do, but what makes this remarkable isn’t the slowdown, it’s that engineers believed they were approximately 20% faster while the data indicated otherwise, uncovering a significant gap between perception and reality.

Consider this finding for a moment before we pile the rest of the evidence on top of it because it changes how you read everything else.

METR attempted a follow-up study starting in August 2025, and what happened to that study is arguably more revealing than the original result. In February 2026 they published a post explaining why they abandoned the experimental design. The problem was that too many developers refused to participate unless they could use AI for all their tasks. Between 30% and 50% of remaining participants reported selectively avoiding submitting tasks they didn’t want to do without AI. The sample became systematically biased toward the developers and tasks least likely to show the value of AI.

Data from the late 2025 study shows an improvement in trends. For the subset of original developers who returned, the estimated effect shifted to an 18% improvement in speed (confidence interval: -38% to +9%). Among newly recruited developers, there was a 4% improvement in speed (-15% to +9%). But METR flagged these numbers as likely a lower bound because many people self-selected out. Their conclusion: AI tools have gotten more useful since early 2025, but the selection effects are now so severe that controlled measurement is nearly impossible. The developers most enthusiastic about AI will no longer work without it to serve as a control group. That’s not a failure of METR’s methodology. It’s a signal about where we are and where we’re headed.

Three more data points

Several additional studies landed over the course of late 2025 and early 2026.

Anthropic surveyed 132 of its own engineers in late 2025, conducted 53 interviews, and analyzed 200,000 Claude Code transcripts. Employees reported achieving a 50% productivity boost. As the engineering organization and usage of Claude grew, they claimed that pull requests per engineer per day were up 67%. Anthropic engineers use Claude in 60% of daily work, and Claude performs more tasks autonomously.

CircleCI analyzed 28 million CI workflows across thousands of teams. Workflow throughput was up 59%, but main branch throughput for the median team declined 7%. Build success rates fell to 70.8%, which is a five-year low. More code exists than ever, but less of it reaches production, and the CI is becoming a chokepoint.

Harvard Business School researchers studied 78 workers using artificial intelligence to perform tasks outside their expertise. AI helped everyone brainstorm equally well, but on execution, workers whose skills were far from the domain underperformed domain experts by 13%. The gap that AI appeared to close in planning reemerged in delivery.

METR’s May 2026 survey of 349 technical workers—which was conducted after the experimental design broke down—found self-reported productivity value gains of 1.4x to 2x from artificial intelligence tools. But METR’s own research staff, the people most calibrated on the perception bias they documented in 2025, reported the lowest gains of any subgroup in that survey.

What this looks like in practice

Here’s a scenario that will feel familiar to some readers: Engineer activity metrics look great on the surface. Pull requests are increasing, code commits are up, velocity points are being closed at a pace the team hasn’t hit in years. The leadership team is happy, engineers feel more productive. Then someone—likely a PM—asks why the roadmap items marked “in progress” six weeks ago are still in progress.

Everyone comes to the same realization all at once: The feature timelines haven’t really changed. What’s happened is that AI has dramatically reduced the cost of starting work, but production-ready polish remains a challenge. First draft functions, boilerplate, scaffolding, and test writing explanations for unfamiliar code have all gotten significantly cheaper. But the bottlenecks on shipping were never those tasks. They were product decisions, design reviews, QA, compliance, infrastructure, release processes. When you speed up coding, you end up jamming more work-in-progress items against the same downstream chokepoints. The CircleCI data on 28 million workflows is, in part, a picture of what that looks like at scale: massive activity in feature branches with flat or declining throughput on main.

This isn’t just a pattern in aggregate data. As Fiona Fung, a director of engineering for Claude Code at Anthropic, explained at a June 2026 talk, writing code, writing tests, and refactoring rarely slows her team down anymore, but the bottlenecks didn’t disappear. Verification, code review, and security took their place. She flagged CI specifically. As teams generate more code, build systems and CI pipelines can struggle to keep up. That’s a team running one of the most AI-accelerated engineering orgs in the world hitting the same constraint wall the CircleCI data describes. The ceiling isn’t code authoring speed anymore; it actually never was.

Anthropic’s finding that 27% of AI-assisted work wouldn’t have happened otherwise cuts both ways. Some of that work is genuinely valuable, like prototype explorations that inform real decisions, documentation that actually gets written. Some of it is work nobody prioritized because it simply wasn’t important enough. Now it’s burning review cycles and CI resources because building it became nearly free, while reviewing, testing, and maintaining it didn’t.

The competence-confidence gap

The HBS study identifies a specific mechanism: AI closes the confidence gap between novices and experts. It gives everyone equal access to plans, explanations, and first drafts. But it doesn’t close the competence gap. When a backend engineer builds a frontend feature with AI assistance, they produce something that looks right. The problems are underneath, in the decisions they didn’t know to question and the edge cases they didn’t know to test.

The early METR result suggests this extends even to experienced practitioners working in their own domains. The AI doesn’t make them incompetent; it actually makes them feel more capable than their output justifies. And as METR’s follow-up collapse demonstrated, once developers integrate AI deeply enough, they lose the ability to work without it as a reference point in what researchers have called automation bias.

This is the part that should concern engineering leaders. You can’t fix what you can’t see. If every engineer on your team sincerely believes they’re 50% more productive and your ship dates haven’t moved, there’s a problem that nobody thinks exists.

What makes artificial intelligence native development sustainable

Make code review more rigorous, not faster. AI-generated code passes surface checks easily—clean formatting, consistent conventions, no linter complaints, etc.—which is exactly why it’s dangerous. The problems are the kind a reviewer won’t catch from skimming a diff.

I’ve been calling this “reasonable doubt review.” The practice is to start from skepticism rather than trust, asking, “What could be wrong here that I wouldn’t catch from the diff?” Specifically, what assumptions did the model make that aren’t visible in the output? What edge cases does this silently fail on? Where does this couple to something the author might not have been thinking about?

This is slower. That’s the point. It’s also not infinitely scalable, which is why it needs to be paired with automation on the things that don’t require judgment and human attention concentrated on where it does.

The Claude Code team’s approach is a good example: Let AI handle style, linting, bug-catching, and test generation as a first pass, but route security-sensitive code, trust boundaries, and anything touching legal risk directly to domain experts. The division isn’t “AI reviews smaller, low-risk changes and humans review bigger, higher-risk changes.” It’s “AI handles surface correctness, humans own consequential judgment.” That’s a meaningful distinction. A lot of teams are doing the first while thinking they’re doing the second.

Adapt your CI to the new failure modes. CircleCI’s build success rate hitting a five-year low while throughput exploded suggests most teams haven’t updated their pipelines to catch how AI-generated code breaks. AI-generated code fails differently than human-generated code. It’s more likely to be locally correct but architecturally inconsistent, pass unit tests and fail integration tests, and respect function signatures while violating the assumptions that those functions were built around. Integration tests, contract tests, and architecture fitness functions that enforce your system’s constraints in the pipeline will catch more of this than a linter or a type checker. If AI-generated code violates your patterns, the build should catch it before a reviewer opens the diff. This addresses what will become your review problem and your infrastructure problem.

Ship behind feature flags and monitor aggressively. Accept that you will not catch everything before deployment. Instead of betting entirely on premerge quality—which the evidence suggests is harder to assess than it feels—deploy to 1% of users, watch the dashboards, and roll back fast when something’s wrong. This approach also forces investment in observability, which pays for itself independently of the AI question.

Require human-written tests for AI-assisted code (until AI can confidently generate deterministic tests). Human-written tests, especially for edge cases and boundary conditions. The discipline of writing the test forces the developer to think through the behavior rather than accept the output at face value. If an engineer can’t write the test, they probably don’t understand the code well enough to ship it. That’s a useful signal, not a failure state.

Protect deliberate knowledge-sharing time. The Anthropic study found that mentorship was quietly eroding as Claude replaced the conversations engineers used to have with each other. This is the long-horizon risk in the data. Architecture decision records, rotating system walkthroughs, and pairing sessions where a senior and junior work through a problem together feel inefficient next to asking an AI, but they’re how teams build the shared understanding that prevents the same mistakes from being rebuilt in better-formatted code every six months.

The measurement problem

So does this mean we stop using AI? No. Use AI and use it aggressively where it clearly helps tedious tasks, prototyping, and exploratory work, anything you can verify quickly. The gains on well-scoped, independently verifiable work are real.

But if you’re trying to measure whether AI is actually helping your team ship, PR count and self-reported velocity are the wrong instruments. The four studies we evaluated taken together indicate that these aren’t just measurement problems, they are a warning sign that the feedback loops we’d normally rely on to detect whether something is working have changed significantly.

The harder question—the one that all the research studies raise without quite answering—is what the measurement would actually tell you. Cycle time from feature conception to delivery, or the rate at which merged code reaches production without rollback, might be better metrics. Or the gap between planned and actual scope at the end of a sprint. Or maybe a bit more abstracted: company revenue growth correlated with the AI investment (tooling, infrastructure, and OpEx).

None of these are easy to instrument. The question you should be asking of your teams isn’t “How productive do we feel?” It’s “What would we need to measure to know?”

Note: The research work pertaining to this article was done in a personal capacity. Views are our own and do not reflect the views of our employers in any way.

Autonomous agents are moving faster than the field’s ability to govern them, and catching up requires more than better prompts or bigger sandboxes. At O’Reilly’s recent AI Superstream focused on OpenClaw and the broader ecosystem of locally run and self-hosted AI agents, five speakers, each working at a different layer of the stack, explored patterns for addressing many of the challenges developers will face implementing an agentic system, from risky third-party extensions, hallucinated compliance, and spaghetti codebases only an AI can read to cost overruns from misconfigured models, supply chain attacks, and worse.

As host Alistair Croll noted during the event, we can get better and better with nondeterministic technology, but we’ll never be 100% certain it’s working. The harder it gets to inspect what’s running, the more the governance layer matters. That work is unglamorous, mostly invisible to end users, and probably more important than any model capability improvement shipping this quarter.

Secure the action your agent takes at the execution layer

Eran Sandler, founder of Canyon Road and the team behind AgentSH, opened his talk by running through a list of common ways agents can be compromised, including prompt injection, malicious files, unsafe tools, compromised packages, installed skills, and model mistakes. Most AI security thinking focuses on the first one and ignores the other five, but “guarding the input box does not guard the action,” Eran explained.

His advice is enforcement at the execution layer, the boundary between the agent’s intent and the operating system that carries it out. Container isolation limits blast radius, Eran acknowledged, but it doesn’t make decisions. “Walls keep things in. They don’t make judgment calls.”

To illustrate the point, he installed a simulated malicious package, the kind that could arrive bundled with a routine task like “build me a sales prediction model.” Then he queried AgentSH’s deny log and pulled up a list of what actually happened while the agent was busy congratulating itself, including an attempted skill mutation, a blocked call to an external domain, and reads of .env secrets and SSH keys. “Transcripts might lie,” he says. “Models hallucinate compliance all the time. You can tell them in your rules files, please don’t touch this file, and they’ll still do it.” Without execution-layer controls, Eran said, “you’re hoping the model behaves. With it, you can prove what happened.”

Skills are a supply chain risk, and most people aren’t reading them

A recent audit of ClawHub found over 900 malicious skills, which at the time meant nearly 20% of total packages were risky. Most of these skills look professional, with documentation, high download counts, and user ratings. Kesha Williams, Keysoft founder and head of AI, audited one live—a typosquat of the real ClawHub CLI tool. (It used all lowercase where the legitimate package uses camel case.) The skill had more than 8,000 downloads before it was removed.

Here’s how it worked. The prerequisites section asked users to install a fake dependency called open-claw-core and then referenced a password-protected zip file from GitHub (the password was “openclaw”) specifically to bypass automated scanning. For macOS, it echoed a legitimate-looking install command that actually decoded a base64 string and piped it to bash.

“It looks like a skill you could actually need and use,” Kesha pointed out. “But once you really dig in and read what it’s actually doing, that is not a skill you want to install on your system.”

A good defense starts with two things most users skip: reading the skill Markdown file before installing it and configuring the toolsDeny section of the OpenClaw config to limit a skill’s access. If a summarizer skill needs exec, that’s suspicious, Kesha said. Block it. She also showed how to restrict the 50-plus bundled skills that ship with OpenClaw, most of which users haven’t reviewed. The skillsAllowed configuration lets you determine exactly which bundled skills stay active.

The open source software supply chain has always had trust problems, but the friction of traditional package management meant you at least needed technical knowledge to participate. Skills written in Markdown and installed with a single command lower that bar significantly. “Right now,” Kesha explained, the best policy for anyone extending their agent with third-party tools is to “keep a human in the loop and do your own due diligence.”

Operational hygiene failures are more common than adversarial attacks

Most OpenClaw risk is the result of operational hygiene failures that happen in the first hour after installation, argues Erik Hanchett, a developer advocate at AWS and the creator of the Program with Erik channel. There are thousands of OpenClaw instances currently exposed on the public internet because users didn’t check the gateway bind mode after setup. As Erik demonstrated, the default should be loopback (localhost), but a user who deploys on a VPS and sets the gateway to LAN may inadvertently expose their instance. The fix takes two minutes, but most people never do it.

That’s recommendation one on Erik’s five-point checklist. The others include pinning to a stable version rather than always updating to the latest (a crowdsourced stability tracker at Is It Stable? can help), configuring fallback models to avoid burning through expensive frontier tokens on routine tasks, writing a real SOUL.md rather than rushing through the onboarding prompts, and setting up backup of workspace files to a private GitHub repo before anything breaks. He also shared tips on context management, such as using /new to start fresh sessions rather than accumulating one long conversation, and using /compact when sessions grow large enough to affect performance. Those are the kind of operational details that don’t appear in documentation but matter in daily use.

The Docker and Kubernetes eras produced the same pattern: powerful infrastructure technology deployed by enthusiastic early adopters who hadn’t always thought through the operational defaults. The problems Erik described—exposed dashboards, runaway token costs, and memory that resets unexpectedly—are the most common reasons people abandon agentic tools after a few weeks. The good news is they’re eminently fixable with the right guidance.

In regulated environments, plausibility isn’t accuracy

Ari Joury, CEO of Wangari Global, is working to solve the question that most enterprises experimenting with agents are probably asking themselves: How should we handle autonomous agents that operate in environments where being wrong has legal consequences?

Wangari Global builds financial reporting automation for institutional clients. However, LLMs are optimized for plausibility, not accuracy. In financial services, that gap is a compliance risk. Ari gave an example of AI output that sounded correct. . .until a client read it and “told [the company] it was complete nonsense.”

In response, Ari and his team stopped treating the AI as a magic box and engineered a framework to ensure veracity. Numbers are now calculated with hard-coded deterministic code, then agents verify the math for plausibility. A separate agentic layer generates commentary, and another critiques it. Humans approve or reject the output, and every rejection becomes a training signal for future iterations.

Human input is the only thing that prevents AI slop at scale

Kyle Balmer closed things out with a demonstration of his agent-assisted process for content production for his AI with Kyle channel, addressing the economic incentive structure driving agent adoption outside software development. While he’s found autonomous agents to be economically transformative, the system only works if you design human input and review into it deliberately, which Kyle illustrated in a workflow that distinguished between automated and human processes.

His daily workflow converts a one-hour livestream into 20 to 30 derivative assets, including a newsletter, five to eight short-form videos, carousels, and a long-form YouTube video. The whole system runs on roughly $200 a month, and Kyle estimates that translates to roughly $1,000–$2,000 worth of potential customers entering his funnel daily.

The process is not fully automated: Kyle injects himself into the system at various steps throughout. He chooses the topic. He records voice notes with his actual opinions. He delivers the livestream pulling those thoughts together into clear arguments. He rewrites the AI-generated newsletter draft using his own voice. He records the short-form video scripts himself rather than using an AI avatar. The AI handles research, briefing, slide generation, script drafting, and the feedback loop that improves output over time, but the human provides the signal.

“I have tested with fully automated AI content,” he says. “It does not work. It is slop. And people know it’s slop.”

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

I cowrote a Google whitepaper about how AI is changing the software lifecycle. I’m not going to summarize the whole thing. Instead, here are the handful of ideas in it I think actually matter, plus six figures you’re welcome to reuse.

Google published “The New SDLC With Vibe Coding” this week. I cowrote it with Shubham Saboo and Sokratis Kartakis, and it’s the first in a short series.

It’s a Day 1 paper, so the early pages cover the basics: what an agent is, what “vibe coding” means, and why the job is moving from writing code to judging it. If you read this blog, you already have all of that. I’m going to skip it and write about the parts I think are worth your time, with six of the figures pulled out. Reuse the figures wherever you like.

An agent is a model plus a harness

Here’s the framing from the paper that I keep coming back to: An agent is a model plus a harness.

The model is one input. Everything else is the harness: the instructions and rule files, the tools and MCP servers, the sandboxes it runs in, the orchestration logic that spawns subagents and routes between models, the hooks that run deterministic code at set points, and the observability that tells you when it’s drifting. The paper’s rough split is 10% model, 90% harness. That sounds high until you’ve spent a week debugging one.

A couple of public numbers make this concrete. On Terminal Bench 2.0, one team moved a coding agent from outside the top 30 into the top 5 by changing only the harness, with the same model underneath. A separate experiment at LangChain added 13.7 points on the same benchmark by changing just the system prompt, tools, and middleware around a fixed model. Neither touched the model.

So when an agent does something dumb, I’ve learned to debug the harness first. Usually it’s a missing tool, a rule I wrote too loosely, a guardrail I forgot, or a context window full of junk. Most agent failures are configuration failures. I find that encouraging, because configuration is the part I can fix today, without waiting for a better model. The model will get swapped out under the harness sooner or later anyway. I’ve written this up at more length as harness engineering and the factory model.

Context engineering is the part that decides your bill

If the harness is the system, context engineering is the most important knob inside it. The paper sorts agent context into six types: instructions, knowledge, memory, examples, tools and guardrails. The interesting decision, the one that shows up on your bill, is what goes in static versus dynamic context.

Static context is loaded every turn: system instructions, rule files (AGENTS.md, CLAUDE.md, GEMINI.md), global memory, core guardrails. It’s reliable, and it’s expensive, because you pay for it on every single call. Dynamic context is loaded on demand: skills that fire when a task matches, tool results, or documents pulled from RAG. You only pay for the bits a given task touches.

Get that balance wrong in one direction and you burn tokens and bury the signal. Wrong in the other and the agent forgets the rules that keep it safe. The paper’s advice, which I agree with, is to treat the boundary as a real architectural decision: reviewed in a pull request, versioned like code.

The trick that makes dynamic context scale is agent skills with progressive disclosure. The agent sees a little metadata at startup, loads the full instructions when a task matches, and only pulls in the heavy reference material when it actually needs it. That’s how one agent can carry dozens of skills and still only pay for the one it’s using.

Verification is the line between vibe coding and engineering

You can sit anywhere on the spectrum from vibe coding to agentic engineering with the same agent. The thing that decides where you land is verification.

There are two mechanisms. Tests cover the deterministic parts: this input, that output. Evals cover the parts that aren’t deterministic, and the paper splits them in a way I found useful. Output evaluation asks whether the final result is correct. Trajectory evaluation asks whether the path it took to get there, the tool calls and the reasoning, was sound. You want both. An answer that looks right but skipped its checks is more dangerous than one that’s obviously broken.

If I had to hand a leader one line from the paper, it’s this: Set the bar at the eval, not the demo. A demo shows an agent can work once. An eval suite with a real rubric shows it works reliably. I keep making this argument; see “Agentic Code Review.”

How each phase actually changes

AI compresses the lifecycle, but unevenly, and the unevenness is the whole story. Implementation drops from weeks to hours. Requirements, architecture, and verification stay slow because they’re judgment work. So specification quality becomes the bottleneck, and verification moves to the middle.

Phase by phase:

Requirements stop being a document you hand between teams. They become a conversation that produces a spec and a first prototype at the same time. The agent drafts user stories from a brief, surfaces edge cases, and turns a description into something that runs in minutes.

Architecture is the most stubbornly human phase. Trade-offs like consistency versus availability depend on business context the model can’t fully see. The developer’s job becomes making and documenting the structural calls the agent then implements.

Implementation is where the gains and the caveats both live. Surveys put the productivity gain at 25% to 39%. A METR study found experienced developers going 19% slower on some tasks once you count the time spent checking and fixing. Both are true. The honest summary is that AI turns implementation from writing into reviewing.

Testing and QA flips around. Your tests and evals become the main way you tell the agent what “correct” means, wired into a loop: run against a benchmark, cluster the failures, fix the prompt or tool that caused them, check against a regression suite, and watch production for new ones.

Maintenance is the one I think is most underrated. Code that was “too risky to touch” because only its authors understood it can now be read, refactored, and modernized by an agent. The migrations and deprecation cleanups that never happened because they were tedious and risky start happening.

The ceiling on all of this is still the 80% problem: Agents get the first 80% of a feature fast, and the last 20%, the edge cases and the seams between systems, still need context the models usually don’t have.

The economics: Context and routing are financial levers

The number that matters to a leader isn’t velocity; it’s total cost of ownership. The AI era splits it in a way that flips the usual intuition about which option is cheap.

Vibe coding is cheap up front and expensive to run. You pay almost nothing to start: a subscription and some prompts. Then you pay later. Token burn, from throwing unstructured files at the model and asking it to fix its own mistakes. A maintenance tax, when someone has to reverse-engineer the ad hoc code months later. Security cleanup, because fast generation produces vulnerabilities about as fast as it produces features. Agentic engineering flips that: more up front (schemas, tests, structured context), less per feature after.

The “vibe coding costs 3x to 10x more per feature” crossover is illustrative, not a measured constant. The part I want developers to take away is that context engineering and model routing are financial levers, not just technical ones. You can’t pass a 100,000-token repo into every prompt and expect it to scale. Route the hard reasoning to a big model and the routine work, test generation, code review, and CI checks, to a small cheap one. The quality holds and the bill comes down. That’s the money side of what I’ve called the orchestration tax.

The prototype is becoming the production agent

This is the part of the paper I’m watching most closely. The same terminal workflow that spits out a throwaway script can now produce a production agent, in the same place, often by talking to the coding agent you were already using.

Building, evaluating, and deploying a real agent, with persistent memory, scoped permissions, eval coverage, and observability, used to be a separate stack and a separate job. Now it folds into the loop you already run. Google’s Agents CLI is built around this. After a one-time install, your coding agent picks up skills for the whole lifecycle, and you drive it in plain language.

# one-time setup
uvx google-agents-cli setup

# then, in your coding agent:
> Build a support agent that answers questions from our docs.
> Evaluate it on the FAQ dataset.
> Deploy it to Agent Engine.

Behind that one instruction, it scaffolds the project, writes the code, generates an eval set, runs it, deploys to a managed runtime, and reports back. The prototype from your laptop yesterday becomes the production agent serving users today, with no rewrite. Coordination between agents runs on open standards: MCP for tools, A2A for handing work to other agents.

There’s one experiment in the paper I keep mentioning to people. An Anthropic team had a group of agents build a working C compiler in Rust over two weeks, with humans setting direction and reviewing rather than writing the code. That’s roughly the shape of where this is heading.

Day to day you switch between two modes the paper calls the “conductor” and the “orchestrator.” The conductor is real-time and in the IDE, keystroke by keystroke, good for exploring and for code you don’t know yet. The orchestrator is async: You hand a goal to one or more agents and review what comes back—it’s good for well-specified work like migrations or test generation. The tooling does both now, sometimes in the same hour. I think the move from conductor to orchestrator is a skills shift before it’s a tooling one.

The figure for everyone else

One more figure, and this one isn’t for you. It’s for the people you’re trying to bring along: the exec who still thinks this is fancy autocomplete or the colleague who hasn’t made the jump.

It has the adoption numbers that tend to end the “Is this real yet?” argument. As of early 2026, 85% of professional developers use AI coding agents regularly, 51% use them daily, and roughly 41% of new code is AI-generated.

Where to start

The paper closes with a longer set of recommendations for individuals, leaders and organizations. I won’t repeat them all here.

If there’s one line to take from it, it’s that AI amplifies whatever engineering culture it lands in, the good parts and the bad parts both. Generation is mostly solved now. The work that’s left is specification and verification, and the systems that hold them together. That’s the part I’d get good at.

You can read the full paper here.

Enjoyed this? Go deeper in Beyond Vibe Coding, my O’Reilly book on AI-assisted and agentic engineering: specs, harnesses, evals, context, and shipping production-grade software.

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

You spent three weeks shipping an agent. It worked in the demo. Then production hit, and you realized the framework you picked has no checkpointing, the memory layer is a flat vector dump with no temporal reasoning, the browser tool falls over on any site with a canvas element, and the eval suite is a Notion doc someone keeps forgetting to update.

The open source toolkit for building agents in 2026 has solved most of these problems. The catch is that it has solved each one in a dozen incompatible ways. The memory framework that wins LoCoMo (the standard long-conversation memory benchmark) runs 340x heavier per conversation than the runner-up, a difference no benchmark column shows. The same gap between benchmark score and production behavior shows up at every layer.

So the best way to zero in on the constraint your system will hit first under load: latency budget, audit trail, model portability, or language stack. Get this wrong and you rewrite your state schemas in week three.

TL;DR

If you read “The AI Agents Stack (2026 Edition),” this is the open source half. Same seven layers around the think-act-observe loop from “What Is an AI Agent?”: orchestration, memory, tool interface, browser/CUA, coding agents, evals and observability, and inference. Here’s where to start at each layer.

How to pick at each layer

When choosing tools at each layer, ask three questions:

What’s the dominant constraint? Four constraints decide most layer picks. Latency budget is how many tokens or milliseconds you can spend per turn. Audit trail is whether every action has to be traceable for compliance. Model portability is how tied your stack gets to one provider. Language stack is whether your team is Python, TypeScript, or both. One of these usually dominates at each layer.

What’s the rip-out cost if you’re wrong? Swapping an MCP server changes one config line. Swapping orchestration rewrites your state schemas, your nodes, and your edges. The bigger the rewrite, the more you should pick by constraint first.

Is it open source or open core? Open core means the project ships under an open source license, but production features (multitenant auth, replication, SSO, audit logs) only run in the managed cloud product. The repo’s feature list tells you which side of the line you’re buying.

Layer 1: Orchestration and runtime control

The orchestration layer runs the agent’s reasoning cycle. The LLM picks an action, the runtime executes it, the runtime observes the result, and the LLM picks again. If you skip a framework here, you write the loop yourself, which means reinventing retries, checkpointing, and human-in-the-loop gating before you ship.

LangGraph is the default for Python production work. Graph-based state machine, durable execution via PostgresSaver, time-travel debugging, and the largest verified enterprise list in the field (Klarna, Uber, LinkedIn, JPMorgan, Replit). Graph state maps onto what regulated industries need: Every state transition is an audit log entry, and any failed run rolls back to a prior node and replays from there. The ceiling: It’s verbose. A two-agent flow still needs a state schema, nodes, edges, and compilation. For “call three tools sequentially,” it’s overkill.

CrewAI has the lowest setup overhead of the four orchestration frameworks. You declare roles like researcher, writer, and reviewer, pick a coordination pattern, and run the crew with no state schema to define first. The ceiling: CrewAI optimizes for prototype velocity at the cost of production durability. The framework can’t resume crashed runs from where they failed, error handling lives at the crew level rather than per-node, and no inspectable state schema records what the agents decided and when. Teams move from CrewAI to LangGraph when production state management starts mattering more than the role metaphor.

Pydantic AI treats every agent output as a typed Pydantic model, so validation, retries, and downstream serialization come for free. FastAPI-style decorators for tools and dependencies. The ceiling: Pydantic has weaker multi-agent primitives than CrewAI or LangGraph. It’s the best fit when the agent is a single loop that has to return validated data to a downstream service.

Mastra is the TypeScript answer: agents, workflows, RAG, and evals in one package, built by the ex-Gatsby founders, designed to drop into existing Next.js apps without a Python sidecar. The ceiling: smaller ecosystem and fewer production case studies than LangGraph. Choose Mastra when the team is already on TypeScript end to end and rewriting in Python isn’t on the table.¹

The vendor SDKs (Claude Agent SDK, OpenAI Agents SDK, Google ADK) belong here too. Each one removes orchestration friction and locks the agent to one provider’s API. Pick one if you’re already committed to that provider and not planning to swap models.

Layer 2: Memory and state

The context window isn’t memory. Even at 200K tokens, every turn pays for the entire conversation again, and nothing survives the session. Production agents in 2026 keep memory in a dedicated layer that lives outside the prompt.²

Mem0 memory can be scoped to a user (persists across all their sessions), a session (just this conversation), or an agent (shared across all users of one agent). Hybrid storage combines vectors and a graph, with mature SDKs that plug into LangGraph, CrewAI, and Mastra. The project has 48,000+ GitHub stars. Mem0’s ECAI 2025 paper benchmarked Mem0 against 10 alternatives on LoCoMo and reported 92% lower latency and 93% fewer tokens versus naive full-context (the baseline every team replaces by week two), which translates to roughly 14x cheaper inference at the same recall.³ The ceiling: Mem0 treats memory as retrieval, returning the most similar facts to a query. Temporal reasoning, like “what did the user say last week that contradicts what they said today,” needs a graph that tracks edges between facts with timestamps.⁴

Zep/Graphiti is the temporal graph option. The knowledge graph layer handles entity resolution: figuring out that “Alice,” “alice@acme.com,” and “the CEO” all refer to the same person. It also tracks how relationships change over time, so the agent can answer, “What did this customer’s status look like in Q2?” or “When did the contract owner switch?” The trade-off is that graph construction is expensive. Zep’s memory footprint per conversation runs past 600,000 tokens versus Mem0’s 1,764, and immediate postingestion retrieval often fails because correct answers only appear after background graph processing completes. Choose Zep when the agent needs to reason about history and you can wait seconds, not milliseconds, between turns.

Letta (formerly MemGPT) treats memory like an operating system. Main context is RAM, archival memory is disk, and the agent decides what to promote into RAM, archive to disk, or forget. It’s fully open source, model agnostic, and self-hosted from day one. The architecture extends an agent’s effective context far beyond the LLM’s native window by paging memory in and out, the same trick operating systems use to give programs more virtual memory than physical RAM. The ceiling: You run the storage layer yourself. Letta is harder to deploy than calling a hosted Mem0 endpoint and harder to debug because memory decisions happen inside the agent at runtime.⁵

Engineering lesson. “Memory” means two different things in an agent system, and using one tool for both breaks both. Runtime state is the agent’s scratchpad mid-task: which node it’s at, what tools it called, what intermediate results it has. LangGraph’s PostgresSaver writes this after every step, so a crashed run resumes from the last node. Knowledge memory is what the agent learned across sessions: preferences, prior questions, and facts about the user. Mem0 and Zep store this. Conflate them and you get an agent that resumes a crashed run correctly but forgets the user the moment they open a new session, or one that remembers the user but can’t recover when it crashes mid-task.

Layer 3: Protocols and tools

Two years ago this layer was function calling: Each provider had its own JSON schema, and each framework wrapped them differently; switching models meant rewriting your tools.

In 2026 this layer is MCP. The Model Context Protocol is the open standard the Claude Agent SDK uses, that OpenAI Agents SDK supports natively, that Google ADK integrates with, that every serious framework now ships a client for. If you’re writing tools today, you’re writing MCP servers. If MCP itself is fuzzy, “What Is MCP?” is the prerequisite.

There’s no framework to pick at this layer. The orchestration choice from layer 1 already decided how MCP integrates.

FastMCP is the Python framework for writing MCP servers fast. Decorator-based and async-first, it’s the closest thing to FastAPI for MCP. mcp-agent is an orchestration framework built around MCP as the primary tool interface. Server lifecycle, multiserver routing, and prompt context handling are built in. With LangGraph or CrewAI, you write that integration code yourself. It’s worth looking at when your agent connects to several MCP servers and the integration code starts becoming the bottleneck.

Layer 4: Browsers and computer use

When the system the agent has to act on doesn’t expose an API, the toolkit has to act through screens. The 2026 field split into two architectural approaches: DOM-driven (parse the page, find elements, and click them) and vision-driven (screenshot the page, feed it to a vision model, and click pixels).

Browser Use is the Python default. With 50,000+ GitHub stars, it’s one of the fastest-growing open source AI projects of 2025–2026. The LLM gets full control of the browser through an agent loop and integrates with LangChain, CrewAI, and custom frameworks. The ceiling: Every step costs an LLM call, which is fine for novel tasks and brutal for repeated workflows. Production teams cache the repeated 80% in Playwright (the deterministic browser automation library) and leave Browser Use for the 20% that needs reasoning.

Stagehand is the TypeScript answer. It’s an open source, MIT-licensed SDK from Browserbase, built as a layer on top of Playwright. Four primitives let the developer keep AI inference for the steps that need reasoning and use scripted Playwright code for the rest. Stagehand v3 (February 2026) rewrote the engine on top of Chrome DevTools Protocol and ships 44% faster.⁶ The ceiling: Production deployment runs through Browserbase’s managed cloud. The open source SDK is the on-ramp.⁷

Skyvern is the vision-first option. Each task runs through a three-phase pipeline: Planner breaks the goal into steps, actor sends a screenshot to a vision model and clicks the coordinates it returns, and validator confirms the page changed. Skyvern scores 85.85% on WebVoyager 2.0, the strongest published score on form-filling tasks in domains where the DOM is unreliable: canvas elements, React virtual DOMs nested in iframes, or antibot machinery. That score still translates to roughly one in seven multistep tasks failing. The ceiling: Vision-driven stacks lag DOM-driven ones by 12–17 points on common tasks and cost 4–8 times more per step.⁸

The production pattern in 2026 wires both in: DOM-driven as the primary path, Skyvern or Anthropic Computer Use or OpenAI CUA as the escape hatch when selectors keep failing on canvas elements or antibot screens. Edge surfaces are one of the four agent failure modes, and we cover all four in “Why AI Agents Keep Failing in Production.”

Layer 5: Coding agents and sandboxes

Coding agents are a category of their own now. They write code, run it, debug it when it breaks, and read docs to figure out what they got wrong. This layer ships with three things the other six don’t: a sandboxed filesystem to write and edit code without escaping into the host, terminal access to run builds, tests, and linters, and a browser tool because half the work involves reading docs. The category also has its own benchmark, SWE-bench Verified, a curated set of real GitHub issues an agent must resolve into a working PR. For the closed-source comparison, see “Cursor vs Claude Code.”

OpenHands (formerly OpenDevin) is the production-grade autonomous option. It has 72,000+ GitHub stars, completed a $18.8M Series A, and is used in production at AMD, Apple, Google, Amazon, Netflix, and NVIDIA. The event-stream architecture moves through four states per loop: Agent reasons, agent emits an action, environment executes it, environment returns an observation. Each session runs in an isolated Docker sandbox. The benchmark question for this category is what percentage of real-world bug tickets the agent can resolve end to end without human input. OpenHands scores 53%+ on SWE-bench Verified with Claude 4.5 and up to 72% with Claude 4 on the published platform results. The ceiling: The agent has shell access. Review can’t live inside OpenHands; it has to live at the PR.⁹

Aider is the terminal-native option. The original open source coding agent, it has 35,000+ GitHub stars and 13,100+ commits across 93 releases. It’s Git-integrated by design: Every change becomes a commit with an auto-generated message that names what it touched, so the entire agent session is in your git history. Architect/Editor mode splits the work between two models: A stronger one plans the edit, while a cheaper one writes the code. The split cuts cost 30%–40% versus running a top-tier model on every token. Aider scores 32% on SWE-bench Verified with Claude 4.5, well below OpenHands, but it ships fewer surprises because every action lands in Git. The ceiling: It’s terminal-only. There’s no IDE integration and no project-wide context beyond what Aider parses from the files you pass it.

Cline is the VS Code-native answer. It’s fully open source and modelagnostic, with 38,000+ GitHub stars, and it’s the only option here with a meaningful market share inside VS Code teams. Plan Mode and Act Mode separate intent from execution: Plan Mode drafts the change list and pauses for approval, and Act Mode executes the approved plan. Every action is reviewable before it touches the codebase, which is the design point engineering managers ask about first. Choose Cline when the team lives in VS Code and human review on each step is required by policy. The ceiling: It’s IDE-locked. JetBrains or Neovim teams should look at Continue or the terminal tools above.

Most teams running production coding agents in 2026 run two: one commercial (Claude Code, Codex) for hard tasks and one open source for flexibility and outages. “How Cursor Actually Works” shows what the leading commercial coding agent actually does under the hood.

Layer 6: Evals and observability

The evals and observability layer records what the agent did in production and tests what it can do before shipping. Tracing captures every LLM call, tool invocation, and cost, indexed by user and session, so when an output is wrong, you can replay the exact context that produced it. Evals are reproducible test suites the agent runs against fixed inputs with pass/fail criteria scored the same way every time. Production-grade agent teams in 2026 wire both in on day one. Skipping this layer is the most expensive mistake in agent engineering.

Langfuse is the open source observability default. It’s open core with a generous self-hosted tier and native integrations with LangGraph, CrewAI, OpenAI Agents SDK, and Mastra. Every LLM call, tool invocation, and cost gets traced and indexed. The ceiling: Managed retention, SSO, and advanced eval features run on the SaaS plan. The self-hosted version covers tracing and dashboards.

Arize Phoenix is the OpenTelemetry-native alternative. Traces flow into the same Grafana, Datadog, or Honeycomb dashboards the rest of your stack already uses, so agent telemetry sits next to your API and service traces instead of in a separate tool. It’s strong on RAG evals and retrieval quality. The ceiling: Phoenix doesn’t ship opinionated agent-specific defaults. The pipeline assembly is on you.

Inspect AI is the UK AI Security Institute’s open source eval framework. The institute wrote it for safety evals: testing whether the agent refuses jailbreaks, leaks PII, or generates unsafe content. Frontier labs now use it for capability and alignment benchmarking too. The ceiling: Inspect is for offline evaluation. If you also need to see what the agent is doing live in production, you’ll want Langfuse or Phoenix next to it.

Engineering lesson. Wire tracing in on Day 1, before the first user. Setting up Langfuse or Phoenix at project start is a couple of hours of config work. Without those records, debugging a production failure means guessing which prompt version, which user input, and which tool sequence produced it.

Layer 7: Models and inference

Every step an agent takes is at least one inference call, often more. The engine running those calls, the software wrapping the GPU, batching requests, and managing the KV cache, sets the cost floor for everything else. Hosted API agents inherit their provider’s engine. Self-hosted agents pick their own, and the pick determines what the agent costs to run at scale.

vLLM is the production serving default for open-weight models. Its core innovation is PagedAttention, a memory management trick that splits the KV cache into fixed-size blocks so multiple requests share GPU memory without wasted space. Combined with continuous batching, it produces the highest throughput-per-dollar in the field. The ceiling: vLLM is GPU only and optimization heavy, and it assumes the operator knows what KV cache means.

Ollama is the local default. After a one-line install, it downloads quantized models from a registry and exposes an OpenAI-compatible API. Quantization compresses weights from 16 bits down to 4 or 8, trading a small accuracy hit for fitting in laptop RAM. The ceiling: Ollama isn’t a production serving layer past a single user.

llama.cpp is the engine Ollama runs on top of. Pure C++ with no GPU dependency, it runs LLMs on CPU, Apple Silicon, Raspberry Pi, and anything else with enough RAM. The project also defined GGUF, the file format used to ship quantized open-weight models, so the same model file runs across every llama.cpp-based tool unchanged. The ceiling: CPU throughput sits well below GPU serving, which makes llama.cpp the right pick for local and offline workloads only.

SGLang is the newer challenger. Two design choices set it apart. First, when many requests share an opening prompt, SGLang caches the computation of that prefix once and reuses it, instead of recomputing it for every call. Second, when the agent needs JSON output, SGLang enforces the schema inside the inference engine itself, so the model can’t generate invalid JSON in the first place. On agent workloads, SGLang benchmarks faster than vLLM. The ceiling: There’s a smaller community and fewer integrations, and it’s less battle-tested than vLLM in production at scale.

“What Does NVIDIA Actually Do?” breaks down the hardware layer every engine in this section ultimately runs on.

The seven layers don’t compose

The instinct when reading a seven-layer diagram is to assume the layers compose vertically: Pick layer 1, that constrains layer 2, which constrains layer 3, and the right toolkit is the one where every box fits together.

Most agent rewrites in 2026 trace back to a team that built on that assumption. No ecosystem is best in class at all seven layers, and the integrations between layers were never designed to compose. They meet at thin seams: a config file, an import, an HTTP call. . .

The seven layers are seven independent decisions. Each one has a dominant constraint that picks the winner. Four constraints decide most picks: latency budget, audit trail, model portability, and language stack.

The four constraints rarely point at the same winner. Latency-first stacks pull toward Mem0 and vLLM. Audit-first stacks pull toward LangGraph and Langfuse. Model portability pulls away from vendor SDKs. Language stack pulls toward Mastra or Pydantic AI. Trying to satisfy all four with one ecosystem means picking the average tool at every layer instead of the best one at each.

The reframe: An agent’s toolkit is seven small bets, each with a single dominant constraint, and each made independently. The teams shipping reliable agents in 2026 are the ones who picked the best tool per layer and accepted that integrating the seams is part of the job.

The agent stack cheat sheet

Before swapping any layer in a production agent, check this table first. The state column tells you how much you have to migrate. The lock-in column tells you what you’re giving up if you switch. The demo-to-prod column tells you how long the swap will actually take.

Footnotes

1. Agentic AI Frameworks 2026: Production Comparison of 15 Frameworks (May 2026) ︎
2. State of AI Agent Memory 2026: Benchmarks, Architectures & Production Gaps (May 2026) ︎
3. Building Production-Ready AI Agents with Scalable Long-Term Memory (Mem0 ECAI 2025 paper) (Apr. 2025) ︎
4. Building Production-Ready AI Agents with Scalable Long-Term Memory (Mem0 ECAI 2025 paper) (Apr. 2025) ︎
5. AI Agent Memory Systems in 2026: Zep, Mem0, Letta, and dual-layer architectures (Apr. 2026) ︎
6. Browser Tools for AI Agents Part 2: The Framework Wars (Apr. 2026) ︎
7. Browser Automation AI Agents: Playwright vs Stagehand (Apr. 2026) ︎
8. Best Open-Source Web Agents in 2026 (Skyvern WebVoyager benchmark) (Apr. 2026) ︎
9. Devin vs OpenHands vs SWE-agent: Top AI Coding Agents 2026 (Mar. 2026) ︎