While tech review articles argue about what PR length is safe to let an agent produce, the review problem is set by a simpler number: how often the agent is wrong.
Specific Labs' Real-SWE benchmark, published September 2026, runs frontier agents on private, licensed enterprise codebases. The best model-and-harness combination resolves 38.8% of tasks, pass@1 averaged over eight runs per task. Everything else scores lower, down to 16.2%.
Read that the other way. The strongest agent setup, on realistic production changes across billing, tax, and multi-service migrations, is wrong on roughly six in ten tasks. Reference solutions touch a median of 11 files, against six in FrontierCode and DeepSWE.
The point for review teams is not the ranking. It is that the acceptance decision is the part that did not get faster. An agent can land a change in minutes. Deciding whether that change is correct, whether it preserves behavior across the other ten files it touched, still needs a human who understands the system. That is why review time climbs even when PRs land: every agent change carries a ~60% chance it needs real correction, and the correction is not free.
Two workflow implications.
