How AI Agents Change Code Review

Code review wasn't designed for agents

The mental model behind code review is simple: a developer made a decision, wrote some code to reflect it, and now a peer is checking whether both the decision and the implementation were sound.

When an AI agent authors a PR, that model falls apart. The "decision" was made somewhere inside a model. The code often looks clean. The reviewer has no way to reconstruct why the agent chose one approach over another — or what context it had when it did.

The result is rubber-stamping. Engineers approve agent PRs faster than human PRs, with less scrutiny. That's the opposite of what should happen.

What you actually need to review

Reviewing agent-authored code well requires three things that a diff alone can't give you:

1. What context did the agent have? An agent that read the wrong files, or an outdated spec, will write plausible-looking code that solves the wrong problem. You need to see what it actually consumed.

2. What did it decide not to do? Agents explore before they commit. The rejected paths — the tools it tried, the approaches it considered — are often more revealing than the final output.

3. What instructions was it following? If the agent had a skill file, a system prompt, or a set of PR guidelines, those constraints should be visible alongside the output they shaped.

A better review workflow

With ChooChoo, the agent trace is attached directly to the PR. Reviewers can see:

• The files and symbols the agent read
• The sequence of tool calls it made
• The guidelines it was operating under
• A summary of what it changed and why

This doesn't slow down review — it speeds it up. Instead of reverse-engineering the agent's reasoning from the diff, reviewers start from a structured account of it.

The goal isn't surveillance

We're not building this so managers can micromanage agents. We're building it so engineers can trust them.

Trust at scale requires transparency. Once you can see what an agent did, you can decide whether to approve it, refine the guidelines that shaped it, or flag a pattern for the team to discuss.

That's what good review looks like — for humans and agents alike.