Designing an AI Agent Workflow with Fallback Chains and Human-in-the-Loop

The article discusses the challenges of building a reliable AI agent workflow, and how the authors implemented a fallback chain architecture to handle failures gracefully without turning every request into a human review bottleneck.

Details

The article describes the authors' experience building an internal document processing agent for a healthcare client. The initial straightforward workflow, where the user uploads a document, the LLM extracts structured fields, validates against a schema, and writes to the database, had a high failure rate due to the compounding of individual step failures. To address this, the authors implemented a fallback chain architecture, where multiple approaches are tried in succession if the confidence in the primary approach drops below a specified threshold. This allows failures at any step to be caught and rerouted without restarting the entire pipeline, improving the overall reliability of the system. The article provides the core Python code for the fallback chain implementation, demonstrating how the concept can be applied in practice.