AI tools can produce a lot of code quickly. That makes testing more important, not less, because the cost of writing code drops while the cost of an undetected defect does not. This guide describes a practical way to test and review AI-generated code so that speed does not come at the expense of quality.
Why code is a good fit for AI, and why it still needs review
Anthropic's guide to building effective agents explains why coding agents work well: code solutions are verifiable through automated tests, agents can iterate using test results as feedback, and the problem space is well defined and structured. The same guide adds an important caveat: automated testing helps verify functionality, but human review remains crucial for ensuring solutions align with broader system requirements.
Both halves matter. Tests give the agent and your team fast, objective feedback. People check what tests cannot: whether the change fits the architecture, the business rules, and the way your system is operated.
Our practical checklist follows. Only the first section is based on the Anthropic guide.
1. Treat generated code as an untrusted draft
Review AI output the way you would review a pull request from someone new to the codebase. It may look correct and still miss a requirement, use an outdated approach, or introduce an unnecessary dependency.
2. Write the acceptance criteria and tests first
Decide what "done" means before the code is generated. Acceptance criteria and tests written first give the agent a target and give you a way to know whether it hit it. If an agent writes the tests as well, review them: a test that was written to pass the code proves little.
3. Keep changes small
Small changes are easier to review, easier to test, and easier to roll back. Ask for one well-defined change at a time instead of a large rewrite.
4. Layer your tests
- Unit tests check individual functions and edge cases.
- Integration tests check that components and external services work together.
- Regression tests protect behavior that already worked.
- End-to-end checks confirm the main user flows.
Include difficult cases: empty inputs, unexpected formats, failures of external systems, and permissions.
5. Put quality gates in your pipeline
Run tests, linting, type checks, and dependency and secret scanning automatically on every change, and require them to pass before merging. Gates apply the same standard to every change, whether a person or an agent wrote it.
6. Review what tests cannot see
A human reviewer should check:
- Fit with the architecture and the conventions of your codebase.
- Business rules, especially edge cases the tests do not cover.
- Security, including input handling, authentication, and secrets.
- Dependencies and licenses that the change introduces.
- Maintainability. Will another engineer understand this in six months?
7. Verify in a realistic environment
Run the change in a staging environment with representative data before production. Use feature flags or gradual rollouts where a defect would be costly, and keep a way to roll back.
8. Learn from defects
When a defect slips through, add a test that would have caught it and adjust the review checklist. Over time your test suite becomes the shared memory of what must keep working.
What AI is good for in testing
AI can help draft tests, suggest edge cases, summarize failures, and explain unfamiliar code. Use it as an assistant to the process above, not as a substitute for it.
If you want a team that works this way, an AI Pod combines AI agents with senior engineering review and release readiness. For the production side, see the AI prototype to production checklist.