How we designed an AI Agent workflow with fallback chains and human-in-the-loop

If you've shipped an AI agent to production, you already know the uncomfortable truth: the works great, but real users find every edge case your prompt didn't anticipate. We ran into this exact problem when building an internal document processing agent for a healthcare client. The agent worked fine 85% of the time. The other 15% ranged from "slightly wrong" to "confidently hallucinated a patient ID that doesn't exist." This post walks through the fallback architecture we built to handle those failures gracefully, without turning every request into a human review bottleneck.