Coding agents ship PRs faster than humans can trust them. The gap is filling up with a debt nobody is measuring — and it's about to swallow your engineering velocity.
Every team in 2026 measures coding agents the same way: PR count, lines of code, cycle time, developer NPS. None of those see the real cost — bloated diffs, weak tests, ambiguous rationale, ownership sprawl, and human reviewers spending more time verifying AI code than they used to spend writing their own.
This talk introduces ReviewDebt: a practical framework for scoring every pull request on the hidden review burden it creates. The scoring is deterministic — diff size, test-coverage delta, ownership spread, generated-code smells, evidence and rationale gaps — so the number is defensible in a real engineering review. We'll walk three real PRs side-by-side (clean human PR, high-debt AI PR, refactored AI PR), watch the scoring play out signal by signal, and look at a 90-day dashboard from a production backend org where review debt climbs in lockstep with AI-PR share.
By the end you'll have:

A working definition of review debt and the deterministic signals that compose it
A scoring rubric you can adapt to your team's review standards on day one
The dashboard view that turns "are coding agents working for us?" into a number your VP of Engineering can act on

2026 won't be the year teams decide whether to adopt coding agents. It'll be the year they decide whether they can trust, review, and govern the code those agents produce.