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 demo 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. The problem with linear agent workflows Our first version was straightforward: user uploads a document, the LLM extracts structured fields, validates against a schema, and writes to the database. A single chain, no branching logic. The failure math killed us. If each step in a 5-step workflow has 90% reliability, your end-to-end success rate drops to about 59%. Add more steps, and it gets worse fast. We needed