Unit, integration, end-to-end and evaluation testing.
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An automated AI review passed front-end work that had missing interactions, no hover states, colors that did not match the design and a laggy page. I think the LLM reviewer is wrong in its concept. It should work like a QA engineer and check the implemented page against the design, so I set up a front-end testing procedure that validates the work from the mockup, step by step.
WordPress 6.9 added assertEqualHTML(), so a test that fails because of whitespace in rendered HTML no longer has an excuse. I reviewed the implementation and mapped concrete migration patterns for plugin and theme test suites, plus the places where assertSame() should stay.
Most multi-agent failures are orchestration failures, and a smarter model does not repair a handoff that never said what done means. Before I tune any model, I would implement four controls: typed handoffs, explicit state contracts, task-level evals and transactional rollback. GitHub's engineering deep dive, published on February 24, shows the same core pattern.
Vibe coding an AI agent means you tweak a prompt, rerun it and ship when the output feels right. The feeling is the only check, and it does not run in CI. I build agents the way I build any other software: modular code, mocked dependencies and tests written before the logic.