Maintaining Code Quality with AI Coding Agents
Maintaining Code Quality with AI Coding Agents
AI agents produce code that looks right. It follows patterns, uses reasonable variable names, and often works on the first try. The problem shows up later - inconsistent conventions across files, skipped edge cases, and subtle bugs that a human reviewer would catch but the agent's confident output discourages scrutiny.
The Plausibility Trap
When code looks professional, reviewers skim faster. AI-generated code is especially dangerous here because it mimics the style of good code without necessarily having the substance. A function might handle the happy path perfectly while silently ignoring three error conditions.
The fix is not reviewing harder. It is making quality checks automatic and mandatory.
Convention Files Are Your First Line
A CLAUDE.md or similar convention file tells the agent exactly how your codebase works. Naming patterns, error handling approaches, import ordering, test requirements - spell them all out. Without explicit conventions, the agent invents its own, and they will be different every session.
Good convention files are specific: "All API calls must have error handling with retry logic" beats "Handle errors appropriately."
Linters Catch What Reviews Miss
Run linters automatically before any AI-generated code gets committed. ESLint, Pylint, SwiftLint - whatever fits your stack. Configure them strictly. The agent does not mind fixing lint errors, and catching style inconsistencies automatically is cheaper than catching them in review.
Pre-commit hooks that block commits on lint failures work well with AI agents. The agent will fix the issues and try again.
Tests Are Non-Negotiable
Require the agent to run existing tests after every change. Better yet, require it to write tests for new code before the change is considered done. If tests pass, you have some confidence the code works. If there are no tests, you have nothing.
The pattern that works: conventions define standards, linters enforce style, tests verify behavior. Each layer catches what the others miss.
Fazm is an open source macOS AI agent. Open source on GitHub.