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AI Code Review Gates for Reliable Agent Work

Geist Labs helps teams make agent-written code safer by requiring second-pass review, behavior-focused tests, error-path checks, and evidence before completion.

Who this is for

Engineering teams using AI coding agents in production repositories where review quality and test honesty matter.

Common symptoms

  • Agent-written tests assert mocks instead of behavior.
  • Happy-path changes ship without enough error-path coverage.
  • Reviewers lack a repeatable checklist for AI-specific failure modes.

Geist Labs point of view

Agents are fluent, not inherently correct. Reliability improves when teams make proof mandatory and route review work through explicit gates.

Practical framework

  1. 01Require tests before implementation for risky changes.
  2. 02Run a second-pass review focused on AI failure modes.
  3. 03Check error paths, evidence, and behavior assertions.
  4. 04Only accept work after verification artifacts exist.

Questions teams ask

How do review gates make AI-generated code more reliable?

Review gates force the agent or reviewer to prove behavior with tests, inspect edge cases, and attach evidence before code is considered done.

What causes nondeterministic AI application outputs?

Weak contracts, missing examples, uncontrolled context, and absent evals make AI outputs drift across runs.