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AI Can Write the Code. Who Verifies It?

mihaisplace.com25 points53 comments
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AI-driven coding agents are driving much faster output but also a measurable rise in defects and review burden: a 2026 study of 22,000 developers across 4,000 teams found 54% more bugs per developer, roughly fivefold longer median PR review times, and over three times as many incidents per PR during higher AI adoption periods, with those figures reported as correlations. The response described here argues that slowing down isn’t practical, so verification must scale: agentic workflows like Superpowers and RoboRev embed planning, TDD, and AI reviewers into local loops, while hosted and integrated reviewers from Claude, Codex, GitHub Copilot, and Cursor provide automated PR analysis and autofix features. The piece stresses model diversity and human accountability, noting that multiple agents can still agree on the same error and that AI review cannot replace independent verification.

To preserve velocity without sacrificing quality, a comprehensive automated verification pipeline is proposed as a superset of existing practices: deterministic checks (linters, formatters, type and build checks), layered tests (unit, integration, end-to-end), security/dependency scans (SAST, secret and vulnerability scanning), agentic code review against spec, infrastructure and database migration validation, and architecture/maintainability analysis, plus optional performance, accessibility, privacy, and supply-chain checks. These steps run under CI/CD orchestrators like GitHub Actions, GitLab CI/CD, or Jenkins, feeding production monitoring back into tests and architecture, with humans retaining final merge authority for high-risk changes.

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