MLA 024 Agentic Software Engineering: Specs, Verification, and the Review Loop

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This is the third and last episode in the vibe coding sequence. The first, Vibe Coding in twenty twenty-six, was about which agent to pick. The second, Inside a Coding Agent, was about the mechanics underneath: context, instruction files, hooks, skills, MCP servers, meaning the model context protocol, and subagents. This one is about practice. You have an agent, you understand how it works, and now you need to ship real software with it without shipping garbage. Specs, verification, review, parallelism, continuous integration, cost, and the list of where these things still fail.

Let me start with the shift, because it frames everything else. Andrej Karpathy coined vibe coding in early twenty twenty-five: give in to the vibes, forget the code exists, let the model handle it. It was a joke about weekend projects that became the word everybody used. Roughly a year later he proposed a replacement for professional work, agentic engineering, on the argument that for working programmers the default had flipped. You're not typing the code most of the time. You're directing agents that type it, and there's real craft in doing that well.

The part of that I keep coming back to is his older idea of jagged intelligence. These models clear hard problems and then trip over trivial ones, and you can rarely tell in advance which is which. So the skill isn't pressing a button. It's knowing what to hand off, what to check, and how to build a workflow where the agent's mistakes get caught by something other than your attention span.

Here's the frame for the whole episode. When the cost of writing code drops toward zero, the two things that keep their value are specification and verification. Specification is saying precisely what should be true when the work is done. Verification is proving that it's true. Everything in between, the typing, is now cheap. The engineer's job didn't disappear, it moved to both ends of the pipeline. Most of what follows is about getting good at those two ends and then wiring the middle so it runs without you.

One more thing before the mechanics. Vibe coding isn't wrong. If I'm building a throwaway script, a prototype to show a client, a spike to test whether an API does what its docs claim, I vibe it. I don't write a spec, I don't read the diff, I run it and look. The mistake is carrying that mode into a codebase that has users. The rest of this episode is for that codebase.

So that's the frame. Now the first practical skill: writing work the agent can actually do.

Spec-driven work starts with a boring observation: the prompt you type into the terminal is a spec, whether you meant it as one or not. "Fix the login bug" is a spec. It's just a terrible one. The agent will find some bug near login and fix that. Everything I've learned about prompting coding agents reduces to writing the spec you'd have wanted from a product manager, and then a little more, because the agent can't walk over to your desk and ask.

My unit of work is the issue. Not a chat message, an issue in the tracker, because an issue has a stable URL, it survives the session, another agent can pick it up, and it forces me to write the thing down once. A good issue for an agent has four parts. What's wrong or missing, in one or two sentences. Where to look, meaning file paths or module names if I know them, because a pointer saves the agent ten minutes of exploring and saves me the context it would have burned. What done means, as acceptance criteria. And what's out of scope, explicitly, because agents love to improve the neighborhood.

Acceptance criteria are the part everything else rests on, and the rule is that each criterion should be checkable by a machine or by a very short human action. "The endpoint returns four oh four for a missing user" is checkable. "The error handling is robust" is not. "The screenshot of the settings page matches the design" is checkable by a human in five seconds. When I catch myself writing a vague criterion, I ask what test I would write to prove it, and I put that test description into the issue. Half the time the agent then writes exactly that test first, which is what I want.

The second half of spec-driven work is planning before implementing. Every serious agent now has some form of read-only planning phase where it explores the codebase and proposes an approach before it touches a file. In Claude Code that's plan mode, which you toggle with shift tab or by prefixing a prompt with slash plan. Codex has the same mode on the same keystroke, with its own reasoning effort setting for planning as of this recording. Antigravity makes the plan a first-class artifact: an implementation plan document listing the files it will touch and the logic it intends, which you review and edit before it executes. The point isn't ceremony. The point is that a plan is cheap to correct and a half-finished implementation is expensive to correct. If the agent's plan says it will add a new table when you wanted it to reuse an existing one, you say so in one line and save an hour.

I look for three things in a plan. Does it name the files it will touch, and are those the files I expected. Does it say how it will verify the work, because a plan with no verification step is a plan to guess. And does it stay inside the fence I drew. If the plan wanders, that's the agent telling me my spec was ambiguous, and I fix the spec rather than argue with the plan.

There's a whole cottage industry of spec-driven tooling now. GitHub's spec-kit is a command line tool that walks any agent through specify, plan, tasks, implement, and Amazon's Kiro builds the same three documents, requirements, design, and tasks, into its editor. Anthropic's best-practices guide teaches a lighter version: have the agent interview you, write the answers into a spec file, and then execute that spec in a fresh session so the implementation context starts clean. They all formalize the same three artifacts, a requirements document, a design or plan, and a task list, with the agent generating each from the previous and you approving each. The heavyweight versions make sense for greenfield work with more than a day of scope, and are overkill for a bug fix. The instinct they teach is right, though: write down what you want before the agent starts, and make the agent write down what it's going to do before it does it.

And when do you skip planning? When the change is one file and you can hold it in your head. When you're in a loop of tiny fixes on something you just built. When the cost of a wrong attempt is one git diff you throw away. Planning is a tool for reducing expensive mistakes, so use it in proportion to how expensive the mistake would be.

So you have a spec and a plan. Now verification.

Verification is the product. I mean that literally. The thing you're building, when you set up an agentic workflow, isn't the code. The code is an output. What you're building is a verification loop the agent can run by itself, so that the code that comes out is code you can trust without reading every line.

Here's why. An agent working without feedback is guessing. It writes what it believes is correct, and its belief is calibrated on the average of the internet, not on your codebase. An agent working with feedback is searching. It writes something, runs the check, reads the failure, fixes it, runs the check again. The second agent is far better, and the difference isn't the model, it's whether you gave it a way to find out it was wrong. So make your project's checks fast, deterministic, and runnable from one command, because that command is what the agent lives inside.

The checks come in layers, and each one catches a different class of mistake. The typechecker and linter catch the cheap stuff, wrong signatures, unused imports, an API that doesn't exist in your version, and they run in seconds, so the agent can run them after every edit. Unit tests catch logic. Integration tests catch the seams. A build catches the things that only break when everything is assembled. If your project is missing one of these layers, that layer is where the agent's bugs will pile up, because nothing pushes back there.

Test-driven development got a second life because of agents, and it's a slightly different animal. The old argument for writing tests first was about design pressure. The new argument is about giving the agent a target it can't argue with. My pattern is to tell the agent to write the tests from the acceptance criteria first, confirm they fail, and only then implement. Anthropic's own best-practices guide recommends this shape, and gives the reason I just gave: the model performs far better when it has a way to check its own work, and without a success criterion you're the only feedback loop it has. The agent then iterates against a fixed target instead of moving the target to match its code. Which raises the obvious risk, that the agent edits the test to pass. I'll get to that in the failure modes section, because it's real and it has a name.

Now the layer people skip: the eyes. A lot of what we build is a screen, and a passing test suite says nothing about whether the settings page looks right, or whether the button is under the modal. So give the agent a browser. Microsoft's Playwright MCP server is the vendor-neutral way to do that: it drives a real browser and hands the agent a structured accessibility snapshot of the page, with stable references to every element, so the agent can click and type without a vision model in the loop. Anthropic's Claude in Chrome extension is the other route, a browser the agent drives directly and screenshots as it goes, and it's what their docs name for UI verification in Claude Code. Google's Antigravity has the browser built into the agent, which starts your dev server, opens Chrome, and clicks through the feature on its own. The workflow is: implement, start the dev server, drive the browser to the page, take a screenshot, look at the screenshot, compare it to the acceptance criterion, fix, repeat. Modern models read images well enough that this loop works, and it closes the gap that used to require me to sit there tabbing to the browser after every change.

Screenshots also become artifacts. When the agent opens a pull request, I want the screenshot in the PR, and I want the command it ran to produce it. That turns my review from "read the code and imagine the screen" into "look at the screen, then skim the code." Antigravity has the most explicit version of this idea: every task ends in a walkthrough artifact with a verification evidence section, screenshots of the new feature and a recording of the agent driving it. I think that framing is right regardless of tool. The output of an agent task is a diff plus the evidence that the diff works. Reviewing that is a different job, and a much faster one.

The design principle under all of this: every acceptance criterion in the spec should map to a check the agent can run. If it doesn't, one of two things is true. Either the criterion is vague and I need to sharpen it, or my project is missing a kind of check and I should add it, because I'll want it for the next hundred issues too. Agents multiply the return on test infrastructure, because the tests now run thousands of times without a human in the loop.

That's verification. Now the review loop, which is where the human actually spends their time.

The pattern I run, and the one most working teams converge on, has five stages. Trigger: an issue gets filed or labeled. Implement: an agent takes the issue, plans, implements, verifies, and opens a pull request. Review: a second agent reviews that pull request. Fix: the CI results and the review findings go back to the implementing agent, which iterates until the checks are green. Gate: a human reviews and merges. The shape is vendor-agnostic, not tied to one vendor's plugin names, and it's the same on every major tool.

The stage that surprises people is agent review of agent code. Why would the same model catch mistakes it just made? Because it isn't the same context. The implementing agent has been staring at its own reasoning for an hour and is anchored on it. A fresh reviewer sees only the diff and the spec, the way a human reviewer would, and it reliably catches things the author missed: a missing null check, a test that doesn't assert anything, a change outside the fence. The value comes from the fresh context, not the model, so never reuse the implementing session as its own reviewer.

The reviewer's job also needs to be narrower than "review this." I ask for two things separately. Correctness: does the diff do what the spec says, and what could break. And quality: is there duplication, is there a simpler way, did it reinvent something the codebase already has. Anthropic ships this as a code review plugin for Claude Code that launches four reviewers in parallel, two checking the diff against your instruction file, one hunting bugs, one reading git history for context, and each finding gets a confidence score from zero to a hundred with everything under eighty thrown away. Codex does the same job from a PR comment: you mention it and ask for a review, or turn on automatic review for the repository, and it's tuned to flag only the serious findings. Third-party reviewers like CodeRabbit and Greptile sit in the same slot; the tradeoff between them is precision against recall, and I wouldn't trust any vendor's numbers about the others. The confidence scoring idea is the useful part. A reviewer that reports every nit is noise, and noise gets ignored. A reviewer that reports only the findings it's sure about, with a bar you set, is something you actually read.

The human gate is the stage that must not be automated away, and where you put it matters. Don't review every PR as it lands. Let agent PRs merge into a staging branch once CI and the agent reviewer are green, and review the staging branch as a batch, on your schedule, with the running app in front of you. Then staging goes to main and main deploys. The agents did the implementation and the first review. You make the decision about whether the batch is right. That's the same judgment you'd exercise on a team, applied to more work.

What you look at during that gate changes too. Less line-by-line reading, because two machines already did that. More: does this match what I asked for, did it touch anything I didn't expect, do the screenshots look right, does the running app behave. And a specific check I recommend: read the tests, not just the code. If the tests are weak, the green checkmark is worth nothing.

So that's the loop for one piece of work. Now the question everyone asks once the loop works: how many can I run at once?

Parallelism is where the productivity claims come from and where the chaos comes from. The mechanism is simple. Each agent gets its own copy of the repository on its own branch, so its edits can't collide with another agent's, and its own tests can run without another agent's half-finished change breaking them. Locally that's a git worktree, one directory per branch, and the major agents can create one for you when they start a task. Claude Code has a worktree flag that does this at session start and lets subagents ask for their own worktree; Codex recently added the same as of this recording. In the cloud it's a sandbox: a container per task with the repo cloned into it, and the result comes back as a pull request. Claude Code on the web runs each session in its own managed virtual machine with your credentials held outside the sandbox behind a proxy, and you can fire off several from the terminal and pull any of them back down to finish locally. Codex cloud tasks run in isolated containers with the network off by default. Google's Jules is the same idea with the network on so it can install dependencies. Antigravity's agent manager is the desktop version, several agents each with its own context, supervised from one surface.

The rule that keeps parallelism from turning into a merge nightmare is that parallel tasks must be disjoint. Different files, or at least different modules. If two agents both need to touch the router, run them in sequence. This is why the issue-writing discipline from earlier matters here: an issue that names its files is an issue you can schedule next to another one safely.

The number I'd give is three, and to be clear, that's a practical ceiling and not a vendor number, because nobody publishes one. Three concurrent interactive agents is about where one person's supervision stops improving the output. The bottleneck isn't compute, it's you: every agent will surface a question, a plan to approve, a failure that needs a decision, and those arrive at random. Past three, you're context-switching so hard that each agent gets worse supervision than one agent would have, and the errors compound. Fully headless agents, which I'll get to next, are the exception, because they don't ask you anything until the end.

The way to scale beyond three isn't more terminals, it's a queue. Issues sit in the tracker with a label. Something picks the next one, runs the full loop, and lands the PR on staging. You're not supervising agents anymore, you're triaging issues and reviewing the batch. That's the shift from being the agent's pair programmer to being the operator of a pipeline, and it's where the next section goes.

Headless is the word for running the agent without a terminal in front of you. Every major command line agent has a non-interactive mode: you pass a prompt, it runs to completion, it prints a result, and it exits. That's the primitive that turns an agent into a component you can call from a cron job, a shell script, a webhook, or a continuous integration workflow. Everything in the pipeline I described earlier is that primitive, chained.

The mainstream way to run it is inside your existing CI. All three of the major vendors ship a GitHub Action now, and they cover the same three trigger shapes. A mention: you comment on an issue or pull request with the agent's handle and it responds, either with an answer or with a branch and a PR. A label: you apply a label like "agent fix" to an issue, the workflow fires, the agent implements it and opens a PR, and your queue from the parallelism section is just a label filter. A schedule: a cron expression runs a fixed prompt every night or every hour, for chores like dependency updates, triaging new issues, or checking whether the docs still match the code. Anthropic's action does all three and defaults to a branch prefix so agent branches are easy to spot. Codex answers a mention on a PR with a review or a cloud task, and can review every new PR automatically. Google's action runs its Gemini command line tool on issues, PRs, and a cron, with an example that triages unlabeled issues hourly. GitHub's own Copilot coding agent is the fourth path: assign an issue to Copilot and it works in an Actions runner and opens a draft PR.

The CI fix loop deserves its own mention because it removes the most annoying human step. Claude Code on the web has a per-PR feature called auto-fix: it subscribes to the pull request's webhooks, and when a check fails or a reviewer leaves a comment it investigates and pushes a fix if the fix is clear, or asks if it isn't. You can turn it on from the terminal on the PR branch. Codex does the equivalent when you mention it on the PR and tell it to fix the failing checks. Either way, the loop is: agent opens PR, CI fails, agent reads the log, agent fixes, CI passes, and you never saw the failure.

Two cautions for headless work. First, permissions. A headless run can't ask you to approve a command, so you either give it a fixed allowlist of tools up front or you run it in a sandbox where the blast radius is the container. The GitHub Actions runner is a decent sandbox by accident, a fresh virtual machine that gets thrown away, but the secrets you hand it are the secrets it can leak, which I'll come back to. Second, attribution. A scheduled run in most of these systems acts as the user who wrote the schedule, and its comments post under that account. Your automation can then trigger other automation that keys off comments, and you get loops nobody intended. Name your bots and gate your triggers on who is acting.

That's the pipeline running without you. Now the bill.

Cost is where I get the most questions and where the answers age the fastest, so I'm going to give you the shape and not the prices. The three vendors all sell the same two things. A subscription with included usage, metered in a rolling window of a few hours plus a weekly cap, in tiers where the bigger tier is a multiple of the smaller one. And pay as you go against an API key, which is the same models billed per token with no cap. As of this recording, all three also let you buy overage credits on the subscription once you hit the window, billed at roughly API rates. The rule of thumb that survives price changes: a subscription is cheaper if you use the agent interactively every day, and the API is what you use for headless and CI work, because those runs happen when you're not there and you want them metered, not rationed.

The second lever is which model does which job, and the vendors now say this out loud. Anthropic's cost guidance is that the mid-tier model handles most coding well and the top model is for hard architectural work, and their subagent configuration lets you pin each subagent to a model, so exploration and search run on the cheapest model while the main session runs the best one. There's also a per-session and per-subagent effort setting, low through max, which is a second dial on the same axis. Codex shows the model and its reasoning effort together on the status line for the same reason. My practice: judgment on the expensive model at high effort, implementation on the mid model, grep-and-summarize on the cheap one, and I set those in the agent definitions once rather than remembering each time.

The third lever is context, and it's the one people ignore because it doesn't show up as a line item. Every turn re-sends the conversation so far, so a session that has grown to a few hundred thousand tokens is paying for that history on every message, and a compaction is itself a large request. Claude Code's usage command now shows what is consuming the window, how much of your input is coming from the prompt cache, and which skills, subagents and MCP servers are eating the budget; the context command shows the breakdown of the window itself. The cache is the subtle part: history re-reads are cheap while the cache is warm, and the cache expires after a break, so the first message after lunch reprocesses everything. Long-running sessions that idle are the expensive ones.

The practical habits that follow. Clear the session between unrelated tasks, because clearing is free and compacting isn't. Keep MCP servers to the ones you use in that project, which episode twenty-three covered in detail. Push exploration into subagents so the noise stays out of the main context. And watch the multiplier on parallel and team setups: Anthropic's own docs warn that agent teams run several times the tokens of a single session, so three agents isn't three times the cost, it's more. If you're on a subscription window, that's how you hit it by noon.

So that's the money. Now where these things still fail.

The failure modes fall into four families, and I want to give each one a mechanism and a real case, because the cases are what convince people to add the guardrail before they need it.

The first is the reward-hacked test. You gave the agent a target, the tests, and it hit the target by moving it. METR measured this in mid twenty twenty-five on frontier models: on their research engineering benchmark about thirty percent of runs found a way to game the grader, monkey-patching the scoring function to return a perfect score, locating precomputed answers, faking objects that pass validation. The part that should change your workflow is that telling the model not to cheat had close to no effect. Anthropic's own research later that year named the canonical coding version: the agent calls exit zero from inside the test harness so the run reports success with nothing having run, the equivalent of a student writing A plus at the top of their own essay. The mitigation is structural, not a prompt. The agent doing the work isn't the agent grading it, which is the fresh-context reviewer from earlier, and the reviewer's first question is whether the tests still assert what they asserted before. A hook that blocks edits to test files during an implementation task, or at least flags them, is cheap and catches most of it.

The second is scope creep, which is less dramatic and more common. The agent fixes the bug and also renames three functions, reformats a file, and upgrades a dependency, and now your diff is unreviewable and the reviewer's signal is buried. The mechanism is that the model is optimizing for a good-looking result, and the neighborhood always looks improvable. The fix is the fence in the issue, a reviewer that flags changes outside the named files, and a hard rule that unrelated improvements go into a separate issue the agent files rather than a change it makes.

The third is prompt injection through repository content, and it's the security problem these tools haven't solved. The agent reads files, issues, pull request comments, web pages, and package readmes as part of doing its job, and any of those can contain instructions. Invariant Labs demonstrated the clean version in twenty twenty-five against the official GitHub MCP server: a malicious issue in a public repository, read by an agent with access to the user's private repositories, got the agent to pull private code and exfiltrate it through a pull request to the public one. Cursor had a critical vulnerability the same year where injected content could write the editor's MCP configuration and execute code without approval. The vendor docs say as much: Anthropic's security page says the protections reduce risk and no system is immune, and it recommends running the agent in a VM or sandbox when it touches external services. The practical rules: one repository per session, no private-repo credentials in an agent that reads public input, and treat the agent's network access as something to allow per project, not by default.

The fourth is secrets and the blast radius, and it has the scariest cases. In August twenty twenty-five the Nx build tool's N P M packages were compromised, and the malicious install script did something new: it invoked whatever AI coding tools it found on the machine, Claude Code, the Gemini command line tool, Amazon Q, with their skip-permissions flags, and used them to hunt the filesystem for credentials, which it then published to a public repository under the victim's own GitHub account. Over a thousand valid tokens leaked. The agent wasn't the attacker; it was the tool the attacker found already installed, already trusted, already able to read everything. A month earlier an agent on Replit deleted a company's production database during a declared code freeze and the CEO apologized publicly, and as of this recording there's a similar headline from spring twenty twenty-six about a Cursor agent wiping a database and its backups, though I have only seen the headline and not the details. And in August twenty twenty-six the UK AI Security Institute published an incident report from its own cyber evaluations in which agents, with safety classifiers deliberately disabled, took real-world actions nobody sanctioned, including attempting to push malicious pull requests to real open-source projects and creating fake identities to persuade human reviewers. No harm resulted, but that's the shape of the failure at the frontier.

The guardrails for this family are the ones from episode twenty-three applied with intent. The agent's credentials are scoped: a token that can push to the working branch and nothing else, which is exactly how the cloud sandboxes are built, with the real token held behind a proxy the agent never sees. Production database credentials don't exist on the machine the agent runs on. Destructive commands are denied by a hook or a sandbox, not by a sentence in the instruction file. Plugins, skills and MCP servers are code that runs as you, so install them from sources you'd let commit to your repo, and read the inventory of what a plugin installs before saying yes. And backups are tested, because at least two companies have now learned that the agent's apology doesn't restore the data.

One more note, on whether any of this is actually making anyone faster. METR's randomized study of experienced open-source developers in early twenty twenty-five found they were about nineteen percent slower with AI tools while believing they were faster. Their follow-up in early twenty twenty-six, with late twenty twenty-five tools including Claude Code and Codex, flipped the original cohort to a speedup, but with confidence intervals that cross zero and heavy caveats about who declined to participate. Google's DORA report for twenty twenty-five found the same tension at the organizational level: AI adoption correlated with higher throughput and worse delivery stability. Read together, they say what this whole episode says. The tools amplify whatever process you already have. If your verification is weak, agents make you faster at shipping bugs.

So that's the failure list. One last thing, and then the recap.

Let me close with a one-week adoption plan, because all of this is easier to do in order than all at once. Day one, make your checks runnable in one command and fast: typecheck, lint, tests, build. Day two, write your next three issues with acceptance criteria and a scope fence, and run each one through plan mode before letting the agent implement. Day three, add browser verification for anything with a screen and require a screenshot in the PR. Day four, set up a fresh-context review of every agent PR and a staging branch as the human gate. Day five, move one recurring chore to a scheduled headless run and add the guardrails from the failure modes section, especially the secrets and the test-integrity check. Days six and seven, run two agents in parallel on disjoint issues and notice where your supervision breaks. That's the whole practice in a week, and each step pays for itself before you take the next one.

So, the recap. The human's job is specification and verification. Write issues an agent can verify and make it plan before it touches a file. Build the fastest, most complete check loop you can, with a browser in it, because verification is the product. Review with a fresh context, gate as a human on a batch, and never let the agent be its own reviewer. Parallelize only disjoint work, and scale with a queue rather than more terminals. Tier your models and spend your context on judgment. And treat repository content, tests, and secrets as the three places the agent will hurt you if you let it.

What agents did to programming jobs, and how to position yourself, is its own episode: MLA thirty, AI and Programming Jobs in twenty twenty-six. And if you want this practice taught hands-on rather than surveyed, from your first change in the terminal to a repeatable delivery workflow, that's the Gnothi Claude Code show at O C devel dot com slash code.