An Acceptance Checklist for AI Coding Agents: Ship Generated Code with Confidence

Why generated code needs its own checklist

AI coding agents do not make random mistakes; they make characteristic ones. Human reviewers evolved instincts for human errors—off-by-one loops, forgotten null checks, copy-paste slips. Agent errors have a different signature: the code reads fluently, compiles cleanly, handles a case that cannot occur, and misses the one that occurs daily. Fluency masks wrongness, which is precisely what makes review harder, not easier.

A checklist compensates for the mismatch between how trustworthy generated code looks and how trustworthy it is. None of the checks below is exotic; the value is running all of them consistently, especially on days when the output looks so clean that skipping review feels safe. Those are the days that produce incidents.

Correctness: does it do the thing, and only the thing?

• Exercise the primary path manually once, end to end, in a running application—not only through tests the agent wrote for its own code.
• Check the boundaries the prompt never mentioned: empty lists, zero, the longest plausible input, concurrent submission, a user with no history.
• Confirm the change touched only the files the task required; unrequested “improvements” are the most common source of agent regressions.
• Verify agent-written tests assert real behavior. A test that mocks everything and asserts the mocks were called passes forever and protects nothing.
• Ask the agent to summarize what it changed, then check the summary against the diff—mismatches reliably point at misunderstandings.

Security and data: the checks that cannot be optional

Security review of generated code has one governing rule: agents reproduce the average security posture of the code they were trained on, and the average is not good enough. Anywhere user input crosses a boundary deserves explicit inspection.

• Every database access uses parameter binding—no string-assembled queries anywhere in the diff, including in one-off scripts.
• No secret, token, or key was moved into code, into client-visible configuration, or into the prompt history itself.
• Authorization is checked on the server for every new route or command; a hidden button is not an access control.
• User-supplied text is escaped or sanitized at render time, and uploaded content cannot execute.
• New dependencies are real, maintained packages you looked up yourself—agents occasionally invent plausible package names, and typosquatters exploit exactly that.
• Personal data collection stayed within what the feature needs and what your privacy commitments allow.

Error handling and observability: assume it will fail at 2 a.m.

Agent code fails optimistically. The generated happy path is usually fine; the surrounding failure behavior is where quality diverges. Read every catch block in the diff and ask what a user actually experiences when that branch runs—an actionable message, or a silent nothing while the error disappears?

Then ask what you will experience. If this change misbehaves in production, will anything tell you? A log line with enough context to reproduce, an error tracker event, a metric that moves? Code you cannot observe is code you will debug by user complaint, and by then the user has often already left. This is also an honesty issue in community settings: builders who show their error rates and fix them earn more durable trust than builders who show only screenshots of success.

Performance and cost: plausible code, pathological shape

Agents optimize for readable correctness, not for load shape. The classic miss is the N+1 query: a loop that reads beautifully and issues one database call per item, invisible with ten test rows, catastrophic with ten thousand real ones. Similar patterns appear as unbounded list rendering, missing pagination, per-request work that should be cached, and images processed at original resolution.

You do not need a benchmark suite; you need one deliberate question per accepted change: what happens to this code at one hundred times my current data volume? If the answer is “nothing good,” either fix it now or record it as explicit, dated debt. Unrecorded debt is the kind that becomes an outage.

Maintainability: will you still own this code in six months?

The final gate is personal: can you explain this change, line by line, to another builder? If not, you have not accepted code—you have adopted a liability with syntax highlighting. Ask the agent to walk through anything opaque until you could re-implement the idea yourself; the explanation either teaches you or exposes a flaw, and both outcomes are wins.

Check stylistic entropy too. Codebases rot when each generated change imports its own idiom—one more state pattern, one more error convention, one more way to fetch. Hold generated code to the conventions your repository already has, and record the conventions in a file the agent reads at session start, so consistency becomes the default instead of a correction.

Builders who work this way ship slower on day one and dramatically faster every day after—because acceptance discipline is what keeps a fast generation loop from compounding into an unmaintainable product. It is also what makes your work worth studying when you share it with the rest of the community.