Reviewing AI generated PRs at org level
The problem
Before AI agents, a team merged maybe 5-10 PRs a day, and a senior engineer could hold "what does this touch" in their head. Now teams are seeing 3-5x that volume. The bottleneck isn't reviewing code quality anymore — linters and type checkers handle that. The bottleneck is impact triage: which of these 40 PRs today actually touch something risky, and which are safe to fast-track? A human can't manually trace dependency graphs at that volume.
This is not a judgment problem (reviewing code), it is a computation problem — which is exactly what should be automated, not left to reviewer fatigue.
The traditional review methods - a human tracing "what does this change affect" on many PRs every day - won't work now. This is what failure mode blast radius analysis exists to remove.
How CXGRD helps
CXGRD implements auto-triage. It automatically calculates blast radius for changes done in every PR and flags them with tags like critical, high, medium and low. PRs with low risk move fast, whereas, the ones with critical or high risk are flagged for real human attention. What gets reviewed at scale is not the change, but the risk.
But this does not replace linting and type checking and actual code correctness still matter. CXGRD itself uses the compilers to verify the correctness of code.