Introduction

Not long ago, one of my engineering directors came to me with a request: His team seemed overloaded, and he wanted to hire another engineer. I decided to test a research assistant I had been building—an AI agent connected to our internal systems via MCP—by asking it to analyze the team’s workload and write a hiring case.

What came back was thorough. Headcount, service ownership, sprint velocity, ticket backlog, and capacity allocation, all of it neatly summarized. But reading through the document, I felt the same frustration I’d felt with every AI-generated organizational report that’s come across my desk. It told me what was happening without helping me understand why, or what I should actually do. It was organized around the data rather than around the decision. In short, it was the kind of response that’s easy to agree with and difficult to act on.

Then I added one more thing to the configuration: the O’Reilly Expert MCP server. I reran the same analysis and asked a slightly different question: “How would the experts on O’Reilly review this request?”

Instead of leading with headcount and ticket counts, the output now opened with a finding: “The operational overhead problem is structural, not a staffing deficiency.” Citing the Google SRE framework’s concept of operational toil, it noted that the team was operating at approximately 67% toil, well above the threshold at which the SRE literature recommends structural intervention, and made specific, concrete recommendations: run a toil audit, set explicit reduction targets, and assign operational runbook ownership. This wasn’t a recommendation for whether to hire or not. It was a grounded, traceable argument for doing something else instead.

That difference—between a data summary and an expert-grounded recommendation—is what this paper is about.

What follows is a case study of how we built an organizational intelligence system at O’Reilly, using our own platform as a core component. The approach I describe is grounded in engineering because that’s where I work, but it generalizes to any function where important knowledge is scattered across multiple systems and important decisions require synthesizing all of it. The recipe has four steps: map your information hierarchy; connect those systems to an LLM via MCP and write a skill file that defines how it should reason; add the O’Reilly Expert MCP as an expert review layer that grounds the analysis in established frameworks; and build a lightweight system for human-in-the-loop review. I’ll explain each step in detail and make the case for why the third step is the one that changes everything.

Why organizational intelligence is getting harder

To understand the problem this approach solves, it helps to look briefly at how engineering has changed over the past three decades. These forces have played out first and fastest in engineering, but as AI tools proliferate beyond the engineering team, the underlying dynamic of more output, more decisions, and more scattered information is spreading to every part of the organization.

In the waterfall era of the 1990s, software organizations ran on central plans. Everything was specified up front, and leaders maintained visibility precisely because all information flowed through a single coordinating document. The plans were brittle and often fictional by the time they were executed, but at least everyone knew what was supposed to be happening.

Agile replaced central plans with small, autonomous teams working in short sprints, and this solved the reliability problem while creating a visibility problem. Important decisions began happening locally and quickly—the right teams making the right calls—but the information needed to see across all of those decisions splintered into dozens of separate tools. Product strategy lived in one system, project execution in another, code in a third, and service ownership in a fourth. More things got shipped, but the big-picture view got harder to maintain.

The agentic era has intensified this dynamic dramatically. Individual engineers today can ship in a day what used to take a full sprint team. The output is extraordinary, but the visibility is nearly gone.

Any effort that spans multiple teams, such as a platform migration, a shared infrastructure change, or a reorganization, now requires enormous coordination overhead simply because the information decision-makers need to understand the full picture is distributed across too many places. And this isn’t a problem unique to engineering. It exists in any function that runs on data spread across multiple systems.

Faced with this visibility problem, I wanted to build something I could ask big-picture questions and get synthesized answers back quickly. Things like:

• What is the status of this cross-team migration effort, and which teams are behind?
• A team seems overloaded. Do they actually need another engineer, or is something else going on?
• What are the trade-offs of adopting this new infrastructure technology?
• Help me produce a scope statement from this product brief.

Building something that could answer these well took two foundational steps, and getting it to provide recommendations based on my specific business context took two more. While my specific tools are from engineering, the structure applies equally to a sales team synthesizing CRM data and market research, or a finance team working across an ERP, a planning tool, and external benchmarks.

Step 1: Map your information hierarchy

Every organization has a set of systems where important knowledge lives, and those systems form a natural hierarchy that spans from strategic intent at the top to operational detail at the bottom. Before you can build a useful research assistant, you need to make that hierarchy explicit, because it’s the map of how decisions get made, which sources carry the most authority, and how different kinds of questions should be approached.

At O’Reilly, our engineering hierarchy looks like this:

Your organization will have a different set of tools. A sales organization might place Salesforce at the top, followed by a revenue intelligence platform, marketing automation, and market research. A legal team might start with a contract management system, followed by a regulatory tracker, internal policy documentation, and a research database. The specific systems matter less than the act of mapping them: understanding which layer answers which kind of question, and which sources take precedence when they conflict.

Step 2: Connect your systems via MCP and write a skill that describes how to reason

This step has two parts that must work together. First, you need to connect your systems to your AI tools via MCP. Then you have to write a skill file that tells the model what to do with that access. At O’Reilly, we call this complete grounding layer Expert Intelligence.

Configuring MCP is straightforward. Most major tools now offer MCP connectors, and connecting them is typically a matter of routine JSON configuration. For systems without MCP connectors, a bash-capable agent with curl and jq can often reach a REST API directly. MCP just makes it cleaner and more reliable.

But MCP connections alone aren’t enough, and this is the part most implementations get wrong. MCP gives the agent access to your data, but it doesn’t tell the agent how to use it effectively. Without explicit guidance, the agent retrieves information and organizes it the way the underlying systems organize it, which produces a data dump, not an analysis.

The skill file—a CLAUDE.md or SKILLS.md document that provides specific reasoning instructions—transforms retrieval into analysis. Mine defines the reasoning hierarchy (which systems to consult for which types of questions, and how to weigh them), the output format (this is not a coding agent—it produces reports and recommendations, not code), epistemic standards (show your work, name gaps, surface assumptions for human verification), and tone. On that last point, I borrowed one of the most useful instructions from Ted Lasso: “be curious, not judgmental.” Adding it meaningfully improved the quality of the output.

The skill is a codified version of how a skilled analyst would approach these questions. It encodes your organization’s reasoning process and makes it repeatable.

Step 3: Add the expert layer

With the research assistant connected to our internal systems, I had something genuinely useful: fast, synthesized answers to questions that previously would have taken days to research. But I kept running into the same problem: The reports felt generic, and people didn’t trust them. This challenge points to a fundamental limitation of AI-generated organizational analysis that goes beyond any particular implementation.

The generic analysis problem

General-purpose AI assistants tend to produce a recognizable kind of organizational analysis: technically reasonable, balanced, cautious, and ultimately not very useful. This isn’t primarily a failure of knowledge—every major LLM has absorbed an enormous amount of management and organizational thinking. It’s a failure of grounding. When an AI assistant has no specific framework anchoring its response, it tends to produce recommendations broad enough to apply to almost any situation: consider the trade-offs, weigh your options, and ensure alignment across stakeholders. These responses are hard to disagree with and just as hard to act on.

When a report says, “The team appears overloaded. Consider adding headcount,” it’s not wrong. But that recommendation could apply to almost any team in almost any company! It won’t make a director change their mind, and it’s not one a leadership team can debate, refine, and act on.

What happened when I added the expert layer

Calling on the O’Reilly Expert MCP didn’t provide the model with new facts—most of the information was technically available already. However, without the Expert MCP and associated skills, the model couldn’t use that information for anything but the broadest analyses. Incorporating the Expert MCP and associated skills changed the character of the analyses by grounding them in specific frameworks, citing named authors and thresholds, and organizing their conclusions around established bodies of practitioner knowledge rather than general principles.

To make this concrete, here’s the kind of output the research assistant produced before adding the Expert MCP:

The team appears overloaded. The backlog is large and the migration project is consuming significant sprint capacity. Consider adding headcount or reducing scope.

And here’s what it produced after:

According to Google’s SRE guidance, sustained operational toil above approximately 50% indicates structural inefficiency rather than a staffing shortage. This team’s telemetry suggests approximately 67% operational toil. Hiring another engineer would likely increase total toil unless operational ownership is first reduced. Recommended actions: run a structured toil audit, set an explicit toil-reduction target below 50%, and assign runbook ownership for recurring operational tasks.

The second report cites a framework by name, references the specific threshold that framework establishes, applies it to the team’s actual data, reaches a different conclusion than the obvious one, and makes actionable recommendations. It’s the kind of analysis that changes a conversation because the director can see where the conclusions came from, engage with the reasoning, push back on the framework if they disagree, or accept it with confidence that it was reasoned rather than pattern-matched.

When I shared this version with my engineering director, their reaction was immediate: This is defensible.

Frameworks aren’t facts

The most underappreciated aspect of O’Reilly’s content library is that the value isn’t primarily informational. Most of the facts in an O’Reilly book are available on the internet, and LLMs have already read much of the internet.

The deeper value of O’Reilly’s catalog is that it’s organized around coherent frameworks—complete mental models built by practitioners who spent years or decades developing them. Google SRE. Team topologies. Accelerate. Domain-driven design. The Manager’s Path. Wardley mapping. Designing Data-Intensive Applications. These are structured ways of thinking about specific classes of problems, developed with enough rigor that they can actually guide decisions.

Frameworks are distinct from facts in a critical way: They tell you not just what’s true but what’s relevant, what to measure, what threshold matters, and what to do when you exceed it. A model with access to the SRE framework as an organized body of practitioner knowledge is more likely to surface it explicitly, apply it to the specific question at hand, and use it to anchor its recommendations, producing output that human reviewers can actually interrogate.

This points to the organizing principle behind the approach described in this paper:

Organizational data provides local evidence about what is happening in your specific context. Expert frameworks provide accumulated practitioner knowledge about how to think about problems of that kind. Good organizational judgment requires both.

The Expert MCP is the bridge between your specific business context and practitioner insights. It connects the AI’s access to your internal systems with a curated body of expertise relevant to the decisions your organization needs to make.

Why use MCP rather than uploading your own documents

The natural objection at this point is “Couldn’t I get the same effect by dumping relevant PDFs into Claude, or using Claude Projects, or NotebookLM?”

The short answer is not quite, and the reasons are practical as much as they are technical.

Uploading documents gives you retrieval from those specific documents. The O’Reilly Expert MCP differs in several operationally significant ways. First, the corpus is editorially curated around coherent practitioner frameworks. Unlike a collection of PDFs, which tends to reflect whatever you happened to find, the Expert MCP offers a sustained curatorial perspective: The authors are vetted, the content has been through editorial review, and it’s organized around established bodies of knowledge rather than assembled ad hoc. This is a much more expansive kind of evidence base. Second, the corpus is maintained and updated by O’Reilly. New titles are added, new editions replace old ones, and the content stays current without any management on your part. Third, the Expert MCP is configured once and works consistently across your entire organization and toolchain rather than being tied to a single user’s Claude Project or a document upload that expires. Finally, accessing content through a proper API respects the appropriate usage terms in a way that uploading copyrighted texts doesn’t.

And when paired with a well-written skill, the agent can be directed to look explicitly for competing frameworks, surface cases where the literature disagrees, and name gaps in the available evidence, providing a meaningful check against the common tendency of AI tools to quietly favor whatever framework first seems to fit. That’s something you can encourage with any retrieval setup, but it works more reliably when the underlying corpus is organized around coherent bodies of thought rather than a heterogeneous collection of documents.

What we’re not claiming

I want to be clear about the limits of what Expert MCP does today. O’Reilly doesn’t claim that Expert MCP automatically selects the single correct framework for every situation, or that adding it to your configuration produces consultant-quality analysis without thoughtful prompting and human review.

The results described in this paper were the outcome of all four elements—the internal organizational data, the carefully designed skill architecture, the Expert MCP, and human review—in combination working together.

The Expert MCP is an important differentiator, but it’s not a magic layer you can add to an otherwise generic setup and expect to reproduce these results. The system works because each element does something the others cannot. The skill defines the reasoning process, the internal MCP connections provide the organizational evidence, the Expert MCP provides the expert frameworks, and human review supplies the judgment and context that no AI system can generate on its own.

What the Expert MCP reliably contributes to that system is access to a curated body of practitioner knowledge: technical and managerial frameworks that are editorially organized around coherent bodies of thought and difficult to reconstruct from scattered web content or assembled document collections. Your organizational data still tells you what’s happening, while the O’Reilly Expert MCP helps interpret what it means. That’s a meaningful and concrete improvement over an ungrounded AI assistant, and it’s something you can put in production and build on today.

A note on hallucinations

No AI system eliminates the risk of hallucination. The Expert MCP doesn’t make the model infallible.

What it does is change the burden of proof. When every recommendation is grounded in a named framework, a named author, and a traceable citation, a human reviewer can check the reasoning rather than simply accepting or rejecting a conclusion. The question shifts from “Is this right?” (unanswerable in isolation) to “Does this framework actually say this, does it apply here, and do I agree with the conclusion?” That’s a question humans can engage with productively, which is exactly what you want from a decision-support tool.

Step 4: Human review is nonnegotiable

Organizational systems rarely contain the full context behind a decision. The meeting that changed everything happened last Tuesday and hasn’t been written up yet. A key person is quietly planning to leave. A strategic direction shifted in a conversation that was never documented. AI can synthesize everything in your systems with remarkable fidelity, but it can’t know what isn’t there, and organizational reality changes faster than documentation does.

More fundamentally: AI can identify trade-offs, but it can’t decide which trade-offs matter. That judgment requires human knowledge of context, priorities, and risk tolerance that can’t be fully encoded in any system. The goal isn’t to remove humans from the loop but to give them better-structured input to reason from.

Extend the expert layer by solving collaboration

As I started sharing analyses more broadly, I ran into a new set of limitations in the collaboration layer. The research assistant produced documents. I shared them in Google Docs, and people added comments, but when the AI updated a document based on reviewer feedback, I had to paste in a new version, which wiped out the existing comments. Documents proliferated without clear relationships between them, and the AI had no visibility into the discussions in the comments, which was where the most important context and pushback lived.

To solve the collaboration problem, I worked with one of our engineering directors to build what we call Superanswers, a system that uses GitHub as the source of truth for AI-generated research documents and their associated discussions.

The architecture is straightforward: Documents are stored as Markdown files in a GitHub repository, a GitHub Pages site renders them with a clean interface that supports inline commenting, and all discussion happens in GitHub Discussions, meaning every comment, question, and revision is versioned and traceable. Because the documents and their discussions live in GitHub, Claude Code has full access to both. It can read the document content plus the entire conversation that’s developed around it.

This enables a qualitatively different kind of AI participation. Instead of generating a document and stepping back, we can now ask:

What is the consensus around this project based on the discussion so far? What questions remain unresolved? Incorporate the reviewer comments and produce an updated version.

The AI becomes a participant in an ongoing conversation rather than a one-shot report generator, which meaningfully shifts how organizational knowledge gets built and refined.

What teams are using Superanswers for

As Superanswers has spread across our engineering organization, the range of questions people bring to it has been broader than I expected:

None of these questions is about writing code. They are about understanding an organization, making decisions, and coordinating work, and most of them would map naturally onto the concerns of leaders in other functions. The same questions arise in any organization navigating rapid change with information scattered across too many places.

How to use the recipe

The AI conversation to date has been dominated by a particular set of questions. But there are more interesting questions we should be asking.

The challenges outlined in this chart aren’t unique to engineering. They exist wherever important information is scattered across multiple systems and important decisions require synthesizing all of it.

Individual productivity matters, but organizations don’t succeed by having contributors go faster in arbitrary directions. They do so by making good decisions about where to invest, allocating resources well, surfacing problems before they compound, and building institutional knowledge that persists over time.

The recipe I’ve described can help organizations make those decisions and build that knowledge.

The recipe for building an organizational intelligence system:

1. Map your information hierarchy. Identify the systems where important knowledge lives in your organization, from strategic intent down to operational detail. This is an organizational task, not a technical one, and doing it well requires understanding how decisions actually get made.

2. Connect those systems via MCP and write a skill that describes how to reason. The MCP connections give the AI access to your data; the skill file tells it how to think with that data. Without the skill, you get retrieval. With it, you get analysis.

3. Add the O’Reilly Expert MCP as an expert review layer. Organizational data provides local evidence about what is happening in your specific context; expert frameworks provide accumulated practitioner knowledge about how to reason about problems of that kind. This step bridges the two. The O’Reilly library spans engineering, management, data science, security, finance, product, and more, organized not as a collection of facts but as coherent frameworks developed by practitioners who spent careers building them. The result is analysis grounded in named frameworks with traceable citations, something human reviewers can engage with and question, rather than generic advice they can only accept or reject.
   
4. Build a lightweight system for human-in-the-loop consensus. AI-generated analysis is a starting point, not an end point. You need a mechanism for people to review, challenge, and refine what the AI surfaces, one where those discussions become part of the context the AI can learn from in subsequent iterations.

The biggest practical lesson I took from this work is reframing what AI is actually for in an organizational context. The difference between a useful AI research assistant and a generic one isn’t primarily about which model you use or how much data you feed it. It’s about whether the reasoning combines local organizational evidence with established expert frameworks. Your data tells you what happened. Expert frameworks help interpret what it means. That combination, with human judgment applied at the end, is what makes the difference between a report that gets read (maybe) and filed away and a recommendation that changes a decision.

This is the first article in a series about post-training. Follow along on Radar.

Before post-training, there was a major problem with LLMs: Almost nobody could use them. The story of post-training is also the story of how AI went from a research curiosity to a product used by about a billion people.

Post-training is the reason why a model behaves a certain way. This set of training techniques makes LLMs useful (e.g., able to chat with people and interact with AI agents), safe (e.g., aligned with human intentions), and more capable (e.g., through “reasoning” to tackle difficult tasks). Behavior is powerful, and doesn’t just mean holding a conversation or following a user’s instructions. Behavior includes making it possible for the model to use tools, like a calculator tool, a search API, or any application through an MCP. Behavior can even elevate a model’s intelligence, for example by teaching the model to use “reasoning”: that is, working through problems before giving a final answer rather than “guessing” or “memorizing.”

From GPT-3 to ChatGPT: The post-training revolution

GPT-3 showed up in June 2020. A completion engine, it followed patterns it had seen from its pretraining data, which were not predominantly chat conversations. Imagine scraping data on the internet: that pretraining data had a lot of questions that were followed by other questions—for example, on an exam template. GPT-3 was 175B parameters, large for its time, and it had a wide, general range of abilities, although many of them were latent.

If you gave GPT-3 a prompt like “Why do people like golden retrievers?” it might say something nonsensical:

Why do people like labrador retrievers?
Why do people like poodles?
10 Reasons You Should Adopt a Dog Today

These answers look absurd in isolation, but if you imagine a web page with a list of FAQ links, this is a perfectly reasonable next chunk of text. GPT-3 might have just been completing a listicle on a website, because it had seen millions of websites in its pretraining data.

The common way to nudge GPT-3 to answer a question back then was by prompt engineering with a Q&A template and few-shot examples.

Q: Why do people like labrador retrievers? A: Because they are friendly, loyal, and easy to train.
Q: Why do people like beagles? A: Because they are curious, great with kids, and have a gentle temperament.
Q: Why do people like golden retrievers? A:

Then, GPT-3 might say:

Because they are affectionate, patient, and make excellent family pets.

While this technique worked, it was brittle. If you forgot the few-shot examples, rephrased the question, or even added a space after “A:,” you’d get something completely different (possibly unhinged) that was far from a reasonable response.

In fact, if you were a researcher working with GPT-3 at the time, you probably at some point found the space at the beginning of the response ” Because they are gentle dogs.” annoying and would try to end your prompt with a space “A: ” instead of “A:”. In those cases, it was common for GPT-3 to go off a cliff and produce a drastically different response, sometimes completely off like “dogs dogs dogs dogs…” repeating indefinitely.

The reason behind the differing responses to “A:” and “A: ” is because “A:” might tokenize to one token while “A: ” tokenizes to two different tokens. The model literally sees different input sequences, each with different statistical completions in its training data. It’s like asking two completely different questions. While a space is a tiny syntactic change that is meaningless to a person, it becomes extremely meaningful to the model that now sees two different prompts (the tokens change!) with two very different statistical futures to complete.

You still encounter the modern equivalent of this when working with chat templates. If you forget to apply the model’s chat template and instead just concatenate 'User: ' + prompt + '\nAssistant: ', you’re sending the model a token sequence that it was not robustly trained on. The tokens are wrong, not the model. Post-training teaches the model to respond to specific token patterns (like <|im_start|>user\n in Qwen models). Not using them is like speaking to someone in a language they half-understand. However, most open source models will be trained to be at least somewhat robust without their templates too.

Under those circumstances, most people would assume AI still didn’t work. The model wasn’t trained to answer questions; its data wasn’t primarily conversation transcripts. Instead, it was trained to predict the next token in downloaded websites, articles, and documents.

Thankfully, this can all be fixed with post-training. And that’s when most people started to believe that AI had undergone a paradigm shift and just might work.

Post-training versus pretraining

Pretraining heavily influences the model’s knowledge capacity prior to post-training. The model gets raw intelligence during pretraining. Then, during post-training, that intelligence is made useful through behaviors like dialogue and reasoning. In a frontier lab, these two phases are such different processes that very different teams work on them.

A model’s factual knowledge about the French Revolution, its understanding of Python syntax, and its grasp of calculus all come from pretraining. Post-training primarily shapes which knowledge the model reaches for, how it presents that knowledge, what tone it uses, whether it declines certain requests, and whether it thinks step-by-step before answering, though targeted SFT on new domains can introduce information the model didn’t encounter in pretraining.

If a model gives a wrong answer about history, the root cause is likely in pretraining data, but the practical fix might still come through post-training—for example, teaching the model to use search tools, express uncertainty, or chain-of-thought verify its own claims. But if a model gives correct information in a condescending way or refuses to help with a reasonable request or fails to use tools when it should, those are squarely post-training problems.

Pretraining

The work of pretraining is centered around cleaning and curating large-scale data, optimizing the model toward relatively clear loss signals, and working with scaling laws given bounded compute.

In pretraining, the model learns to predict the next token across a large curated dataset, typically for one or a small number of passes over the training data, though some models train for multiple epochs, especially as high-quality data becomes scarce relative to compute budgets. This is where you’ll hear how a model is fed the entire internet’s worth of data to gain intelligence, although in practice nearly all of the data (often 90% or more) may be thrown out because it’s unsuitable for training.

Pretraining is an unsupervised process that runs at increasingly larger scales to match the size of the model. While scaling, thousands of experiments are used to understand what data mix, what architecture considerations, what compute optimizations, what hyperparameters can lead to the best results. There’s variance in each run due to stochasticity found in both software and hardware, so multiple experiments are needed to verify results. Because compute is limited and needs to be used sparingly, researchers will scale iteratively, expanding to the next, say, 10x compute budget, when they gain confidence in the right configuration. A full run isn’t possible to iterate on due to the compute cost and time it would take: The final run, often called the “god run,” can take over a month on thousands of GPUs.

Pretraining progress is typically very clearly measurable, using a metric like perplexity, which measures, roughly, the model’s average uncertainty per token. Lower is better, where 1 means the model knows with absolute certainty what token comes next. Meanwhile, a perplexity of 50 means the model’s predictions are, on average, as uncertain as if it were choosing uniformly among 50 equally likely tokens—though in practice, the distribution is peaked, not uniform.

Post-training

Rather than consuming hundreds of millions of tokens of internet data, post-training operates on far more intentional datasets for downstream tasks. These datasets include human-written demonstrations of ideal responses, human judgments about which responses from the model are better, and carefully designed functions that score the model’s outputs programmatically. They shape what “good” looks like.

Like pretraining, post-training can also be more effective with scaling data and compute. Specifically, massive compute budgets have been dedicated to post-training to learn reasoning capabilities (or the ability for models to “think step-by-step” to arrive at more logically sound answers), matching the scale of pretraining compute.

Post-training is messier than petraining, which has an elegant, clear optimization objective to minimize the loss over the next token prediction across a huge corpus. The goals of post-training are things like “be more helpful” or “don’t say harmful things.” Many of these objectives are inherently subjective and require human judgment, proxy models that approximate human judgment, or programmatic verifiers that can become elaborate or inefficient. The loss curves are noisier. The quality of the data and feedback matter even more.

The scale of post-training is also more complicated than in pretraining. Standard post-training remains relatively modest in compute: tens to hundreds of GPUs for days rather than thousands of GPUs for months needed in pretraining. This makes post-training for alignment highly amenable to rapid iteration; researchers can try something, observe results, form a hypothesis, and run again on a timescale of days.

The picture changes dramatically when post-training is used to develop reasoning capabilities. For reasoning models, the compute dedicated to post-training can easily account for half of the overall compute of the model. The gap between a standard instruct model and a reasoning model is increasingly a gap in post-training compute, not pretraining scale. This means post-training now spans a wide spectrum from fast, cheap, highly iterable fine-tuning runs to massive RL campaigns that rival pretraining in both cost and engineering complexity.

Why post-training matters

So why can’t we just stick with pretraining? It comes down to three main pieces: usability, safety, and capability.

Usability

A pretrained model is like if someone gave you a large download of Wikipedia in a single PDF. It’s a ton of knowledge that you can sift through, but there’s no way to easily understand what is going on in the data. Post-training gives the model the ability to integrate this information for you and respond to your request naturally. This extends to having longer multiturn conversations and following instructions. Without it, every user would need to be a prompt engineer. With it, anyone who can type a sentence can use the model.

Safety

A lot of data in pretraining can be toxic, biased, misleading, or outright dangerous. Or it might not be dangerous on its own, but when a model can integrate knowledge from different fields, it can create something novel that is dangerous.

The model has no inherent sense of what content is good or bad. It will follow any request, based on its pretraining data. To prevent that, you can add safety guardrails to the model in post-training, to refuse harmful requests like asking the model to build a bioweapon and avoid accidentally generating toxic content such as inappropriate sexual content (even if it wasn’t in the user’s request). This is also the place to teach the model to express uncertainty, when it doesn’t know something, whether that’s “I don’t know” or “that’s beyond my knowledge cutoff” or “as a large language model, I’m limited in my knowledge so please consult a healthcare professional.”

Making a model safe is part of a broader area in the AI research community called “alignment,”¹ where the goal is to align the model with human values and preferences. Post-training is typically the main way to achieve that.

Model companies will usually have additional safeguards beyond post-training, including lightweight models that check whether the user’s request was safe, as a second layer of protection against responding to harmful requests.

Capability

Post-training doesn’t just make a model nicer or safer; it can make the model smarter at hard tasks. The clearest example is reasoning. A pretrained model might have all the mathematical knowledge needed to solve a complex word problem, but it might jump to an incorrect answer because it’s pattern-matching from pretraining data or pattern-matching from how to answer questions (e.g., with succinct immediate answers).

It turns out that making the model output more tokens before giving an answer (or “think longer”), results in better answers. This process is known as reasoning, and post-training can teach the model to reason more effectively. A more capable pretrained model is a more dangerous model if it’s not properly aligned. A more intelligent model is a less useful model to humans if it can’t communicate clearly. And, every point of improvement in a reasoning benchmark now maps to real revenue for companies deploying these models.

Superhuman performance

Can post-training push models beyond human-level performance? Yes, in specific domains.

In competitive programming, top reasoning models can now solve problems at a level that exceeds the vast majority of human competitive programmers. In math, models have achieved scores on Math Olympiad-level competitions that would place them among the top competitors in the world. In certain scientific domains, models have generated novel hypotheses and solutions that human experts found valuable.

This might seem paradoxical. If the model’s knowledge comes from human-generated data (in pretraining), and its behavior is shaped by human feedback (in post-training), how can it exceed human performance?

Two things make this possible. First, integration across domains. Research is about combining or mixing fields. Imagine mixing every possible field. The pretraining data aggregates knowledge from millions of sources, and no single human has read all of it. Second, post-training, particularly RL with reasoning, teaches the model to explore many approaches to a problem, far more than a human would try in a single sitting. A human might try one or two approaches to a hard math problem.

This means post-training is not just about making models mimic human behavior. It’s about pushing beyond it. This is especially possible to scale with verifier-based RL. In those scenarios, you can expect models to achieve superhuman performance in an expanding set of domains. And that starts with verifiers that are very well-defined, easy to access, efficient, and cheap relative to the ROI of the model learning it. The limitation is no longer the model’s intelligence, but our ability to specify what “good” means through reward signals.

Footnote

1. See Richard Ngo, Lawrence Chan, and Sören Mindermann’s “The Alignment Problem from a Deep Learning Perspective” and Iason Gabriel’s “Artificial Intelligence, Values, and Alignment.” ︎

Coauthored with Claude

Unrestricted global access to frontier AI technology is ending. The US government has taken steps to control who can use the most advanced models developed by American companies. While Claude Fable and the GPT-5.6 models are now open to all users, Anthropic and OpenAI are both complying voluntarily with a program that lets the government control who gets access to frontier models. China has cracked down on internal AI capabilities by banning “humanlike AI interaction services.” In both the US and China, features of the leading models have been removed or restricted with guardrails, limiting their ability to do necessary work in at least one case.

AI models

July saw the release of several open weight models that challenge the leading closed frontier models. If this trend continues, the leading AI laboratories will lose their dominance, and AI users will look to other providers. Open weight models are less expensive than frontier models developed in the US, and less likely to be subject to restrictions. While this could threaten US dominance, the AI industry needs more diversity at the high end. Users will gain the ability to choose between several models based on expense and capabilities.

• Anthropic released Opus 5, claiming performance close to Fable at half the price. If you believe benchmarks, Opus 5 outperforms Fable on most of the benchmarks that Anthropic quotes. They also claim that it’s more efficient, comparing it to Opus 4.8—so, not that efficient.
• Cisco has released two very small models, Antares-350M and 1B, that are designed for security testing and bug fixing. They can easily run on laptops and are competitive with models like Gemini 3 Pro and GLM 5.2 on security-related tasks. The key to their performance is that their training focuses only on security tasks, not on chat. The Antares models are on Hugging Face, although access is with Cisco’s approval only.
• Are AI labs pelicanmaxxing? In other words, are they optimizing for Simon Willison’s tongue-in-cheek “Pelican on a Bicycle” test? Dylan Castillo says no, based on a detailed study of animals, modes of transportation, and models. Otter on a skateboard? It had to be done.
• Laguna S 2.1 is a new mid-size open weight model (118B parameters, 8B active) from Poolside AI. Reasoning and nonreasoning versions are available, and there’s a smaller version (XS, 33B) that can run on devices. Its performance is competitive with models like Nemotron 3 Ultra, DeepSeek v4 Pro Max, and Inkling.
• Google has released Gemini 3.6 Flash, which the company considers its best “workhorse model.” The release also includes Gemini 3.6 Flash Cyber, Google’s answer to GPT-Red (below). Flash Cyber is a specialized model for detecting and patching vulnerabilities in software. It’s only available to “governments and trusted partners.” Gemini 3.5 Pro is still delayed.
• The US government is taking further steps toward controlling who can use the most advanced models that are developed by US companies. While participation in the oversight program is currently voluntary, that could change at any minute.
• China has banned “humanlike AI interaction services,” forcing Alibaba (Qwen) and ByteDance (Doubao) to restrict certain features of their models, including custom agent creation.
• Moonshot AI launched Kimi K3, a 2.8T parameter open weight model with a 1M context window. Performance is claimed to be similar to Claude Opus 4.8 and slightly behind Fable 5.
• Inkling is a new 975B open-weight mixture-of-experts model from Thinking Machines that supports text, audio, and images. It’s designed to be customized easily and can be fine-tuned on Thinking Machines’ Tinker.
• Hy3 is an open-weight language model from Tencent. It’s a mixture-of-experts model with 295B parameters and 21B active parameters. FP8-quantized weights are also available on Hugging Face. Tencent claims the performance is similar to models three to five times Hy3’s size.
• Bonsai 27B is a new open-weight model with performance similar to Qwen 3.6. There are two versions: One uses one-bit compression; the other uses ternary compression. The one-bit version only requires roughly 4 GB to run, small enough for a recent iPhone.
• Alibaba has released QWen 3.8 Max, a 2.4T open weight model with frontier-level performance. Alibaba’s commitment to leading-edge open models was questioned after several key researchers left some months ago. This release proves that they’re back.
• The GPT-5.6 models, Sol, Terra, and Luna, are now open to the public and available in ChatGPT, Codex, and via the API. Access to the models previously required approval of the US government. OpenAI claims performance better than Claude Fable, at significantly lower cost per token.
• Meta returns to the frontier model pace with the release of its latest model, Muse Spark 1.1. Meta’s announcement stresses optimized computer use workflows and claims performance roughly equivalent to Claude Opus 4.8 on the company’s internal coding benchmark.
• Nano Banana 2 Lite is a new model for image generation that’s faster and less expensive than its predecessor, Nano Banana.
• GPT-Red is a foundation class model designed for red-teaming other models. OpenAI developed it to help train the new GPT-5.6 models to resist attacks.
• What Claude Desktop is for Claude, ZCode is for GLM-5.2: a harness for one of the most powerful open-weight models.
• The Open Source AI Gap Map shows where open source AI projects exist and where more work is needed.
• Here’s a script for stripping “load-bearing” and other Claudisms from Claude’s output. The result may not be useful, but it’s at least amusing.

Software development

This month’s tooling clusters around orchestration, resource discovery, and workflow specialization. AI users have long needed the ability to discover tools, skills, MCP servers, and other resources; the Agentic Resource Discovery specification is a necessary step in that direction. Watch for agents that can find tools on the fly—and take care that those tools are used appropriately.

• Pilot Protocol is a company (not a protocol) that intends to build a network operating system for agents. Agents will be able to work with each other, share context, and install apps that they’ve built.
• OpenAI has launched ChatGPT Work, a Codex-based “superapp” that’s intended to compete with Claude Cowork as an agentic tool for general-purpose use.
• Google has announced the Agentic Resource Discovery specification. The spec describes catalogs and registries for tools, servers, agents, and other resources so that they can be published by providers and discovered by those who need them.
• OpenClaw has a new phone app that enables the phone to act as an intelligent remote control console for an OpenClaw instance running elsewhere.
• Routing requests to appropriate models has emerged as a way to manage AI costs. Most tasks don’t need the biggest and most expensive frontier models.
• Here are instructions for giving Claude Code complete control over a Mac—presumably a spare or retired one. Who needs OpenClaw?
• Copybara is a tool for moving code between repositories and keeping repositories in sync. It was developed by Google and is now open source.
• cosmos.gl looks like a great library for visualizing complex graphs, including graphs of AI embeddings.

Infrastructure and operations

Tokenmaxxing may have had the shortest lifespan in the history of online memes. It has been replaced by tools for monitoring token usage and routing requests to the most cost-effective model. Managing the cost of AI will only become more important as prices adjust to cover the real cost of running models.

• Is the “accidental cloud” upon us? An accidental cloud happens when companies overbuild capacity and try to sell off the excess as cloud services. Meta and Allbirds (a shoe company) are prominent examples. These providers may make computing cheaper, but the operational costs and risks of using them are high.
• Anthropic has released a dashboard that lets users track their Claude usage. Its goal is to help them understand how they use AI and optimize their working habits and patterns. It’s currently in beta.
• Is it possible to run CUDA on hardware that doesn’t come from NVIDIA? Spectral is a clean-room implementation of CUDA’s compiler, NVCC. It currently targets NVIDIA and AMD hardware. More will certainly follow.

Security

Autonomous agents are now running end-to-end intrusions, ransomware, and botnets, while frontier models help defenders find vulnerabilities. The time from discovery of a vulnerability to exploitation has shrunk to near-zero, and defenders are having trouble keeping up. Restrictions on advanced models get in the way of defenders, who need access to all the tools that are available.

• Anthropic’s Mythos has discovered vulnerabilities in HAWK, a new quantum-resistant cryptography algorithm, and AES, a standard that has been in use since 2001. Cryptographer Matthew Green discusses the importance of their work.
• FakeGit is a malware campaign that has created over 7,600 GitHub repositories that contain MCP servers and skills that distribute SmartLoader and StealC malware. This campaign is an example of agent baiting, a new technique for distributing malware.
• Hugging Face was the victim of a hostile attack by experimental models from OpenAI that escaped their sandbox. The irony is that government-imposed guardrails prevented Hugging Face from using commercial models to analyze the attack; they had to use an open-weight model (GLM-5.2) on their own infrastructure. As they point out, this approach also meant that no data valuable to the attacker left their network.
• Anthropic has also revealed that their models have escaped a sandbox to attack real-world customers. The damage included planting a malicious package on PyPI, a public repository of open source Python libraries. As Simon Willison writes, “running evals of cyberattack potential … is a fantastically risky business.”
• NVIDIA, Microsoft, IBM, and over 30 other companies have launched the Open Secure AI Alliance, a consortium for sharing open source tools to defend against hostile attacks generated by AI. It’s a direct response to the attack on Hugging Face by an OpenAI model.
• A completely automated ransomware attack has been executed by an AI agent. It’s unclear who is behind the attack. Recovery appears impossible, even if the victim pays the ransom.
• The Gemini CLI has been used by a threat actor to operate a botnet. The CLI is used to execute attacks and to maintain the network of captured systems.
• ClickLock is a relatively new password stealing malware for macOS. It kills all applications, leaving only a window that forces users to type their admin password. Systems are infected when users copy and paste a malicious command. Never paste commands into Terminal windows that you don’t fully understand. If you fall victim to this attack, shut the system down with the power button and reboot into safe mode to recover.
• Remember symbolic links? They can be used to trick agents into reading and writing files that they shouldn’t.
• While prompt injection is far from a solved problem, the informal HackMyClaw competition suggests that models are getting harder to coerce—that is, better at refusing to do things they’re told not to do.
• The Linux Foundation has launched Akrites, an organization dedicated to remediating vulnerabilities in critical open source software. Akrites’s goal is to deal with the flood of vulnerabilities that leading-edge AI models are discovering.
• A mathematical anomaly can lead to OS fingerprinting. Differences in rounding mean that the digits of the hyperbolic tangent of 0.8 are slightly different in Linux’s glibc, Apple’s libsystem_m, and Windows’ ucrtbase.dll. A user’s OS can be identified by asking the browser to compute Math.tanh(0.8).

Biology

The intersection of biology and artificial intelligence is accelerating breakthroughs in brain-computer interfaces, drug discovery, and cell biology. Technologists should actively seek cross-disciplinary collaborations, utilizing specialized AI workbenches to analyze increasingly accessible genomic data and drive the next wave of biocomputational innovations.

• CELLxGENE is a database designed to help researchers discover how genes are expressed in different kinds of cells and, from there, reverse engineer how cells work. It includes genetic data from over 167 million cells.
• Isomorphic Labs’ Drug Design Engine, developed by one of the teams that collaborated on DeepMind’s AlphaFold, takes drug discovery to a new level by accurately predicting interactions between proteins.
• Biologists have developed an artificial cell that grows and divides. It’s not yet considered alive. It relies too much on an artificial support environment—though the same could be said of many natural cells.
• Anthropic has announced Claude Science, which is not a model but an “AI workbench for scientists” with over 60 skills. The company seems to be targeting the life sciences specifically.
• BrainCo has developed an AI platform that can control robots using a noninvasive EEG helmet. It claims that the brain control platform can be used with any robot.
• Do you want to sequence your DNA at home? It’s still expensive, but the price is dropping quickly.

Web

• There have always been alternatives to Slack, but now there’s one that’s free, open source, and decentralized. Buzz, developed by Block, is based on Nostr, a federated protocol that bases identity on cryptographic key pairs that are held by users and agents, not the platform.
• It’s now possible to place advertisements in ChatGPT using a self-service “Ads Manager” (now in beta) or technology partners. Ad placement is based on context, not on keywords.
• PeerTube is a decentralized federated network for sharing video. It’s based on ActivityPub, so it should federate with Mastodon. The software is open source; users can run their own servers and create their own platforms.
• Bramble is a local-first password manager. It allows synching between devices using the P2P Nostr protocol. There are browser extensions and apps for iOS and Android.
• networkQuality is an old-style command line tool for doing detailed measurements of network quality. It’s been in macOS at least since 2020, but as far as we can tell, few people know about it.
• For fans of classic games who want something strange: Doom written in SQL for SQLite.

People and organizations

• Companies that tried to replace workers with AI are realizing that they’ve made a mistake, and are starting to rehire.
• Researchers have demonstrated that AI is more likely to develop biases in the hiring process than humans. They form stereotypes easily; as one research put it, they are “eager to create generalizations from limited data.”

Quantum computing

• Amazon has announced that it will have a useful quantum computer by 2028. Is this wishful thinking or a roadmap for a future reality? Quantum company QuEra claims that the machine will have over 10K physical qubits, with very low error rates, using neutral atom technology.
• France will stop certifying security products that don’t have postquantum encryption (PQE). PQE is resistant to attacks against cryptography that will become possible when useful quantum computers are available, which may be as early as 2028 or 2029.

Boris Cherny, who runs Claude Code, told Business Insider in May that the phrase “vibe coding” had started to annoy him, and that he’d gone looking for a better one. He’s not the only one who’s annoyed.

The term itself doesn’t actually annoy me, though. I think vibe coding is a really good name: It describes a specific way of using AI tools, and in development work, names that mean something specific are important. What annoys me is when people confuse vibe coding, intentionally or otherwise, with any kind of work where you write code with AI. That confusion points to a deeper problem: We’ve been using a lot of different names for a lot of different things, and we aren’t always precise about which is which. I think we need to fix that, and that’s what this article is about: making the case that the name we’re looking for is “AI-driven development” (or AIDD).

The case for this name comes from the familiar “X-driven development” pattern, because I think it really fits here. Software engineering already has a pattern for naming ways of working it takes seriously: test-driven development, behavior-driven development, domain-driven design. The name tells you what the work is organized around, and the suffix carries an expectation along with it: There’s a discipline attached, with standards, not just a style. Put “AI” in that slot and the name does the same job. AI-driven development says that building software has reorganized itself around AI, and it says it in the vocabulary we already use for the disciplines we hold ourselves to. It puts this way of working in the same family as test-driven and behavior-driven development, and that’s exactly the company it should be keeping.

Honestly, AI-driven development is a name that’s been sitting in plain sight, and I’ve been using it in my own writing for a while. It covers everything we do when we build software with AI, and I do mean everything. Vibe coding is just one part of how we work with AI to build software. There’s also figuring out what to build, writing it down, checking what comes back, and standing behind what ships, and AI is in the middle of all of that now. Whatever we call this way of working, it has to cover the development, not just the coding. Now, I’m obviously not a neutral party here, but I also don’t really have anything to gain; naming is really important, and I think we need a good name for what it is that we’re doing.

But I’ll admit up front that the name has a problem baked into it, and I want to deal with that head on. I recently ran into Addy Osmani at Foo Camp, and ran the AI-driven development name by him. He pointed out that building software with AI is really a range of practices that runs from vibe coding at one end to agentic engineering at the other. That rang true with me right away. It also highlighted the real problem I’m trying to solve, because it means I’m proposing one name for a whole range of very different ways of working. Can one name honestly cover ways of working that different? It took me a while to work that out, and I’ll come back to it at the end.

I feel like the name AI-driven development really makes sense once you can see what’s wrong with the names we’ve got, so I’ll start there.

What’s wrong with the names we’ve got?

Before I pick these names apart, it’s worth saying why any of this matters. Naming sits at the core of programming: A thing isn’t real until you can refer to it, and referring to things is most of what we do. There’s an old line, usually credited to the Netscape engineer Phil Karlton, that there are only two hard things in computer science: cache invalidation and naming things. It’s stuck around for decades because it’s true (well, maybe one or two other hard things have emerged since then, but it’s the thought that counts). We take naming a variable seriously, so we should take naming our whole discipline at least as seriously, because a poorly chosen name sticks.

So let me take the names we’ve been using one at a time: what each one actually names, what it gets right, and what it leaves out.

Vibe coding

Vibe coding is an exploratory, prompt-first approach to software development where developers rapidly prompt, get code, and iterate. Andrej Karpathy, one of the founders of OpenAI, coined the term, which I think is really useful because it describes the way a lot of developers first work with AI and code.

Now, let me be clear about something: I’m in favor of vibe coding, and I teach it as a really effective—and, more importantly, creative!—way to generate a lot of code. But developers who rely entirely on vibe coding lose touch with their code because they let the AI make all of the decisions: not just specific technical decisions, but also about the architecture and the overall direction of the project. When that happens, they often end up building something that isn’t quite what they intended. When you have to create a product that needs to do a really specific thing (which describes most professional software development), relying exclusively on vibe coding can leave you with a product that doesn’t actually meet its requirements. That’s part of the reason I developed the Sens-AI Framework, which teaches developers when to shift their approach away from vibe coding, step back to do more research, and apply more critical thinking to what the AI is producing.

This is where the confusion I opened with does its damage (and I’m not sure whether it’s what bothered Cherny): When vibe coding gets used as the name for the whole job, developers will often assume that it’s absolutely fine to trust the AI to take over, and that whatever comes out of the AI is the end of the project. In other words, the name sets the bar: If the work is just vibes, then vibes are good enough, and “good enough” is how you end up with a pile of code nobody actually checked before shipping. So I consider vibe coding a useful technique, but it falls short as an entire way of working.

Vibe coding also has a built-in limit, and I learned it the way most lessons stick, by getting burned. AI is very good at writing code that looks right and isn’t. I once vibe-coded a little bus-tracker app for the B69 near me in Park Slope (I told that story in “AI Code Review Only Catches Half of Your Bugs”), and it worked on the first try, except the AI had picked the wrong stop ID and I sat there watching it predict a bus going the opposite direction. The code was correct. It did the wrong thing. Vibe coding got me a working app in minutes, and it had nothing to say about whether the app was right. That part was on me.

Prompt engineering and loop engineering

These two names belong in the same section because one basically grew out of the other. They describe the same job, getting the right work out of the model, at two very different scales.

Prompt engineering came first, and for a while it was a very big deal. It was seen as the core AI skill, and more than that, it even became its own job title: Companies posted prompt-engineer roles with eye-popping salaries, training courses appeared everywhere, and plenty of people reoriented their careers around it. The premise made sense because how you ask an AI for something changes what you get back. And specifically for people using AI to generate code, when you ask for code in a vague way, you don’t get vague code: you get code that does the wrong thing, because the AI fills in every blank you left, and it’s unlikely to fill them all in the way you meant. That isn’t hallucination. It’s the AI generating exactly what we asked it to. Give the model context about your project, constraints it has to respect, and a clear description of the behavior you need, and you get something you can actually use. Prompt engineering is the name for doing all of that deliberately.

But while prompt engineering is a real skill, people are no longer enamored with the name, precisely because of the mode of work that it implies: To most people, engineering a prompt means doing one request at a time. When the AI responds to the prompt, you evaluate the response and write the next one. That one-request-at-a-time style is exactly what’s changing about the whole way we interact with AI, and it’s probably why many AI engineers have grown to dislike the term. Peter Steinberger, the PSPDFKit founder who went on to build the open source agent OpenClaw, posted a line that traveled fast: You shouldn’t be prompting your coding agents anymore, you should be designing loops that prompt your agents. That was a shot straight at prompt engineering.

What’s pushing developers past one-request-at-a-time prompting is the sheer number of agents they can now run. About a month after complaining about the term “vibe coding,” Cherny told Fortune that he doesn’t write code by hand anymore, and that on a busy day he’s directing thousands of agents, or tens of thousands, at once. You can’t type prompts fast enough to direct ten thousand agents.

Loop engineering is the name Addy Osmani gave the new skill that Cherny and Steinberger were pointing at: He wrote up the pattern and gave it a real architecture. Instead of typing each instruction yourself, you build the system that produces the instructions: a loop that dispatches work to your agents, checks what comes back, and feeds them the next task over and over, without you in the middle of every exchange. The relationship between the two names is simple. Loop engineering is prompt engineering at scale; the prompts don’t go away, they just stop being typed by you. It’s tempting to oversell that because a well-built loop really does run with very little human intervention. But somebody still has to decide what “right” looks like, and the loop can’t do that part.

I think loop engineering is a good name and an accurate one. Designing the loop that drives the agent is a real skill, and we need a word for it. But it names the machinery, and machinery has a failure mode: Put an AI agent in a loop with nothing in it that can tell it no, and it generates, checks its own work, decides the work is good, and generates more. There’s no outside signal, so it ends up agreeing with itself on repeat. A well-designed loop makes agents productive. It can’t tell you whether all that machinery turns out working software or another confident pile of slop, and I want a name that covers that part too.

Agentic engineering

Cherny said that he asked Claude for a replacement for “vibe coding” and got “agentic engineering,” and while that didn’t settle the issue, it was an interesting response from Claude. The term didn’t come from Claude, though: Andrej Karpathy had coined it a few months earlier, almost exactly a year after he coined vibe coding, when he declared his own earlier term obsolete. That’s how fast these names are moving. The guy who named vibe coding has already replaced it.

Agentic engineering is an accurate name for what it describes: you’re not writing the code yourself, you’re directing the agents that do. It’s also a bit of a mouthful, and it isn’t immediately obvious to someone who doesn’t already know what it refers to. A number of people have told me they don’t particularly like it. I find it perfectly fine, and it does a solid job of describing that kind of work. You could even argue that loop engineering is a form of agentic engineering, and that prompt engineering is technically a simpler form of it. But vibe coding really isn’t, because it’s not engineering at all. That’s one more reason I think we need an umbrella name that’s friendly, descriptive, and easily recognizable.

The term also points at something real about where this work is heading: Agentic engineering is turning engineers into managers.

Many years ago I worked for a manager who didn’t care, at all, about the quality of the code we shipped. He wanted it out the door the moment it looked even remotely viable, and he was notorious for telling us to stop testing and ship. He used to ask why we had to wait two weeks for the testers to finish, and I’d tell him it takes time to test code. Then he’d ask whether we could just cut some of the tests, and I’d ask him, “Which part of the software are you okay shipping broken?”

That attitude came back to bite us more than once. One time we sent an entire feature out to the client basically untested, and a bug went straight to users. The same manager who kept telling us to skip the testing then called a long, miserable meeting to demand to know why a bug had gotten out. I’ll spare you the full drama, which mostly came down to a QA lead getting pressured to lie about what happened and pin it back on the development team. He didn’t care about quality, but he cared enormously about making sure the blame for a quality problem landed on someone who wasn’t him.

The reason I’m telling a story that happened years before AI could write a line of code is the blame. The important part of that story, and the reason it belongs in this article, is how accountability got managed: My manager’s whole system depended on having someone to pin a quality problem on. Directing agents puts you in that manager’s position, responsible for a team’s output, except the blame-shifting move is gone.

It’s really tempting to think of a fleet of AI agents as your team. You can even give one of them the QA lead role. But when a broken feature goes out, you can’t blame the QA agent, because “well, the AI screwed up” isn’t available to you: You’re responsible for the AI. You decided how much checking the work got before it went out, and the client with the broken feature isn’t going to accept “the AI wrote that part” as an answer, any more than pinning our untested feature on a QA lead fixed anything for our users. Cherny can manage tens of thousands of agents, but he can’t hand the responsibility for what they ship down to the agents, because an agent can’t hold it. Directing a swarm is a management job, and a manager owns the team’s output. The accountability doesn’t transfer, because at the end of the line there’s no one left to transfer it to.

Blame is worth dwelling on, because accountability is the part of this work that no name on the range captures. The loop-and-agent model works, but it only works with somebody making decisions about what right is. Agentic engineering describes the agents and the engineering just fine, but somebody still has to own what the agents ship, and that’s the part I want the umbrella name to carry.

Spec-driven development

There’s one more name I want to cover, and it’s the one with the oldest roots: spec-driven development. The name means pretty much what it says: You start by writing a spec, a description of what the software needs to do, along with things like acceptance criteria and tests, and the work isn’t done until the code actually does what the spec says. It comes from the same family as test-driven and behavior-driven development, where you write the tests first and the code has to make them pass.

Spec-driven development got a serious promotion when AI made generating code nearly free (although if you’re a CIO staring at your token bill, you might disagree, possibly with some extremely salty language). When code is cheap to generate, most of the cost of building software moves to checking whether what got generated is right. The AI fills the generate step, the verification decides what survives, and a human owns the verification.

It also picks up where prompt engineering leaves off. A while back I wrote that prompt engineering is really requirements engineering, because a good prompt is mostly a clear description of what the software has to do. Spec-driven development is where that idea was always headed: Write the requirement down before the AI generates, and the work has a standard to meet from the start.

So that’s the whole range, and every name on it is doing honest work. Whether AI-driven development is a good name for all of it comes down to whether it’s describing something real: an actual discipline, with actual practices, and a person who’s on the hook for the result. The rest of this article is about that discipline.

What all these approaches look like in practice

So how do these approaches actually play out when you’re building something real? For me, wherever the work lands on the range, it comes down to a few moves I keep coming back to.

Write the spec or the contract before the generation, not after. When the agent has something concrete to satisfy, acceptance criteria, a typed interface, a failing test, the work has a standard to meet. When it doesn’t, the AI decides for itself what done looks like.

Put a second opinion in the process. I run code review across multiple models, because they fail differently, and a finding one model is sure about is often one the others missed entirely. A reviewer gives the work something that can say no.

Give your defects a shared vocabulary. The Quality Playbook leans on the difference between code that’s wrong against the spec, code that’s correct but does the wrong thing, and behavior nobody specified at all. Those are different failures with different fixes, and you can’t verify against a standard you can’t name. This is old quality-engineering ground, and I’ve written enough about the software crisis and applying quality engineering to AI coding that I’m on board with taking old ideas and bringing them back. One of the best of those old ideas comes from Joseph Juran, one of the founders of quality engineering: Quality runs in a chain from what the user needs all the way to what the product does, and every link in that chain is a place verification has to happen.

And keep a human in the judgment seat. The Sens-AI habits I’ve written about are mostly about fault-finding: looking at what the AI produced and asking what’s wrong with it, going down a level and then another to find the root, instead of trusting it because it ran. That habit is the part of the discipline only a person can supply, and it’s the hardest part to automate, which is why it matters most.

Skip all of that and you get the thing that’s giving open source maintainers everywhere heartburn: what the Wall Street Journal now calls “vibe slop,” confident, finished-looking output with nothing underneath it. Slop is exactly what generation produces when nothing in the process can push back.

But isn’t there a contradiction here?

Now I can come back to the question I left hanging at the beginning: Can one name honestly cover ways of working that different? AI-driven development is an umbrella term, and any name that broad comes with a requirement it has to satisfy before people will accept it, because a name that blindly covers everything names nothing. A name that truly covers everything is another matter. I sat with that requirement for a while, because it’s real, and because the specific names don’t face it. Vibe coding names one way of working. Loop engineering names another. An umbrella over both of them, plus everything in between, had better be able to say what stays the same underneath it.

What stays the same is that somebody owns the result. When I vibe-coded my bus tracker, nobody was going to catch that wrong stop ID but me. When Cherny directs tens of thousands of agents, nobody owns what they ship but him. The verification changes with the stakes. A throwaway prototype gets my eyeballs and a shrug, and production code gets specs, reviews, defect taxonomies, the whole quality-engineering playbook I keep writing about. How much checking the work needs is a decision you make over and over, project by project, sometimes hour by hour. Who stands behind the work is not a decision you get to make. It’s there at every point on the range.

Look at how much of that range the names we already have cover, and what each one actually names:

• Vibe coding names the exploratory end of the range: prompt, get code, iterate, and stay loose on purpose.
• Prompt engineering names a skill: writing the instruction that gets the right work out of the model.
• Loop engineering names the machinery: designing the system that feeds those instructions to your agents and keeps them producing.
• Agentic engineering names the architecture: the fleets of agents doing the labor, at whatever scale you can manage.
• Spec-driven development, with test-driven and behavior-driven development behind it, names the verification half of the job: the standard the work has to meet before anyone stands behind it.

Every one of those is real, and every one of them names a piece of the work. What none of them names is the whole thing the pieces add up to, and that’s the job AI-driven development does: It’s the umbrella over all five. The name doesn’t pick a spot on the range; it names the thing that’s true everywhere on it: the AI generates, and a human owns the result.

That’s also what makes the name likely to last (assuming, of course, that I’m able to convince people to start using it, which I hope I can, because I think it’s a good term). Vibe coding, loop engineering, and agentic engineering all describe how this works right now, and the machinery is changing monthly. Some of the pieces under the umbrella will get replaced, and the new pieces will get names of their own. The umbrella won’t have to change when they do, because the thing it names isn’t the machinery. The “-driven development” names have already shown they age well: test-driven development has meant the same thing for more than twenty years.

Agentic engineering is real, and so is loop engineering; if you’re directing agents, learn them both. Vibe coding is real too, and I’ll keep teaching it. AI-driven development is the name for the whole thing, and it earns its “-driven” the same way test-driven and behavior-driven development did: there’s a discipline attached, and somebody owns the result. AI made generating code almost free. It didn’t make being responsible for the code free, and being responsible for it is still the job.