Start with existing tools. Don’t reinvent the static analyzer. If a rule can be reliably enforced with a linter, don’t waste an agent’s time. Static scanners are still more robust for things like code style and basic bugs. AI agents don’t yet beat them in terms of precision. However, where scanners fall short, like false positives or noisy results, agents can help. They’re pretty good at summarizing, explaining, and even validating findings to make them more digestible for humans.
Use agents as sparring partners. When evaluating architecture or code consistency, agents can offer fresh perspectives. Sure, you might not agree with everything they say. But then again, when was the last time a code review didn't include a disagreement? Some agent-generated insights have made it all the way into my architecture roadmap.
AI agents excel when tasked with one very specific job. If a recurring issue keeps popping up in production or testing, that usually means it's ripe for automation. Some of these problems are best caught with static analysis, but for more dynamic or nuanced issues, a narrowly focused AI agent can shine. Define what it should check for, and it usually finds it. This can be a quick win for adding automated QA steps.
When the problem is recurring but messy or complex, a single AI agent won’t cut it. But a group of agents with a bit of control logic just might. For example, no single agent is going to read your use cases and verify if all critical scenarios are covered in your tests. But split the task up, add some coordination, and suddenly things look possible. Decomposition is the first step to automation.
