How to Trust Your Agent: Data Migration in Healthcare

Healthcare organizations increasingly integrate AI into data migration, but a key challenge is establishing trust in the outcomes. A recent project involved improving and transforming healthcare data using AWS Bedrock, Lambda, and Strands Agents to manage agentic loops. The essential lesson was combining traditional data processing, agentic investigation, and transformation with validation, confidence-based routing, and human review to create a trustworthy data assurance process.

Deterministic code remains crucial for controlling decisions. Using agents only when contextual reasoning is necessary helps keep deterministic work deterministic. Deterministic validation handles baseline checks, like validating an organization code, while agents are introduced only when deterministic checks fail. This control is vital because agents can hallucinate, calls can be expensive, and data accuracy in healthcare is non-negotiable.

To prevent hallucinations, strong prompting, explicit grounding, and fallback instructions are essential. Tools should perform one bounded operation, accept validated inputs, and expose only necessary capabilities. Structured output, defined with Python Pydantic models, helps validate and detect errors in agent responses. This narrows acceptable output while still allowing for semantic validation when needed.

Human-in-the-loop (HITL) processes are necessary for reviewing, modifying, and signing off the final data transformation. Agentic systems can handle most assurances automatically, but human review is reserved for cases where confidence thresholds aren't met. The system stores and displays evidence and reasoning, keeping accountability with the human reviewer.