Preventing technical debt in agentic PRs

CI passing doesn't mean your codebase is safe when an agent submits a PR. I've seen too many teams treat these diffs like human code, which is a fast track to massive scope drift and edge-case bugs. The issue is that LLMs are optimized to look correct; they produce code that passes linting and basic tests but fails miserably on null inputs or unexpected types.

If you just hit merge because the diff looks clean, you're accumulating technical debt under the guise of productivity. I've encountered several "refactors" where the agent accidentally introduced logic that only works under ideal conditions, completely ignoring edge cases. Worse, they often sneak in extra functionality that wasn't even requested in the original prompt—total scope creep that makes maintenance a nightmare.

I now run a mandatory 15-minute manual sanity check on every agentic PR. I don't just skim the changes; I specifically hunt for "empty input" traps and verify that the unit tests aren't just passing by coincidence. You have to ensure the test suite is actually validating the new logic rather than just confirming the syntax is valid.

I documented my specific checklist and a walkthrough of a synthetic agent PR where this process caught a production-breaking bug. If you're trying to scale your workflow without losing control of your repo, it's worth looking at.

https://skinahan.github.io/ai-coding-agent-review-kit/example_agent_pr_walkthrough.html?ref=community-devto-walkthrough

How are you guys managing the review overhead? Are you actually trusting the agents or just doing a full line-by-line audit every time?