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.
Speakers:
- Sachin Gupta: Sachin Gupta is a Staff Software Engineer with 15+ years building backend platforms at internet scale, currently focused on the runtime trust boundaries that LLM coding agents blur and the creator of HeapLens, a Java heap analyzer extension used in 50+ countries.
LinkedIn: https://www.linkedin.com/in/guptasachin1/
GitHub: https://github.com/sachinkg12
