Where should trust checks happen for AI coding agents?
Summary
The author explores the critical question of where trust checks should be placed in AI coding agent workflows—before coding, during coding, before PR, or during review—and invites developers to share where trust broke in their actual use of tools like Claude Code, Codex, and Cursor.
Similar Articles
Where should the trust boundary for AI coding agents live?
The article discusses defining trust boundaries for AI coding agents in software development, questioning their authority over critical actions like merging code.
How to Trust Your Coding Agents More (2 minute read)
A practical guide on how to trust AI coding agents more, emphasizing an 'inverse pyramid' of effort: codebase improvements, static analysis, prose guidance, then human review. It links to Cursor's official plugins repo for installing agent-friendly tooling and workflows.
How do you know when an AI coding agent is actually done?
The author built OpenPitStop, an open-source tool that independently checks and verifies the work of AI coding agents, demonstrated on a broken application, and invites discussion on how to trust AI changes.
Where should humans stay in the loop when AI agents perform autonomous coding tasks?
Discusses optimal placement of human review in autonomous AI coding agent workflows, considering trade-offs between automation and safety, particularly for risky systems like auth, payments, and database migrations.
AI coding agent output verification in 2026: read the diff, vibe check it, merge
A reflection on current practices for verifying AI coding agent output, noting that developers often skim diffs and merge without fully auditing the agent's session activity, raising concerns about code review culture in the age of AI.