In this article, you will learn how to design AI agents that can reliably self-correct by grounding their feedback loops in external verification rather than the model’s own judgment.
Topics we will cover include:
Why self-correction in language models only works when the agent has an external signal to check against, and when it isn’t worth the cost.
How to build a code-generation agent with a real test-based verifier, a bounded retry loop, and a structured escalation path.
How to add a consistency-based confidence gate that generates an independent second solution to confirm correctness before shipping.
