Category · 7 of 37 articles
What the category actually is, in terms an engineer can argue with.
Trail of Bits spent six months having agents build an LSP, decompiler, static analyzer, and Lean proof model before the Miden review even started. That is where the leverage landed.
Anthropic made claude.ai 3x faster in two weeks by putting a model inside a measure-build-steer loop. An eval-grounded look at what the sprint proves about agentic workflows.
When AI writes 30% of your lines, patch-text review hits a ceiling. The fix is an execution layer, not a bigger reviewer.
I ran zizmor 1.29.0 against the exact Snowflake GitHub Actions workflow. A deterministic static rule flagged the injection at High confidence while AI review cleared it.
AI code review statistics for 2026: adoption, trust, review turnaround, AI code volume, and bug-catch benchmarks — every stat linked to a primary source.
AI code review vs static analysis compared: determinism vs reasoning, false positives, SAST coverage, cost, and why mature teams run both.
AI code review explained: how LLM reviewers work, what they catch and miss, how they differ from linters and static analysis, plus sourced adoption data.