A platform team watched AI-authored pull requests climb past a third of their weekly volume. The code compiled and the tests passed — but review had quietly become a bottleneck, because no one could tell why a change had been made the way it was.
The reviewer's blind spot
When a human writes a change, a reviewer can usually reconstruct intent from the surrounding conversation, the ticket, or a quick desk-check. When an agent writes it, that context lived in a chat session that closed minutes later. The diff shows what changed; it says nothing about the alternative the agent rejected, or the constraint it was honouring.
The result is a familiar tax: reviewers either rubber-stamp changes they don't fully understand, or stall the PR while they reverse-engineer the reasoning. Both are expensive, and both get worse as AI authorship grows.
Make the why a first-class artifact
Kodela captures the reasoning at the moment of authorship — the actor, the problem being solved, the reasoning, and a risk rating — and attaches it to the exact files that changed. By the time the PR opens, the reviewer isn't starting from a blank diff; they're reading a change that explains itself.
- Each changed file carries a structured why, not a guessed-at commit message.
- Risk ratings route attention: the high-risk file gets the careful read, the low-risk one gets a glance.
- Sign-off gates make the high-risk changes explicit instead of hoping a reviewer notices.
Review stopped being archaeology. The context arrived with the code.
What changed
With context arriving alongside the diff, the team could triage by risk and spend their attention where it mattered. The numbers below are illustrative of the shape of the change teams report, not a guarantee — but the direction is consistent: less time spent reconstructing intent, more spent actually reviewing.