Human-in-the-loop constructs for agentic workflows in healthcare and life sciences

AWS has released a comprehensive technical guide for building AI agents in healthcare that can automate complex workflows while adhering to strict regulatory and safety standards. The post, created in collaboration with the Strands Agent Framework team, outlines four distinct architectural patterns for implementing human-in-the-loop (HITL) controls. These methods are designed to let AI agents process clinical data, submit filings, or automate coding, but automatically pause for documented human approval at critical junctures—such as before deleting a patient record or modifying a drug trial protocol—to ensure compliance with Good Practice (GxP) regulations and protect sensitive Protected Health Information (PHI).

The four complementary approaches offer flexibility based on risk and workflow needs. The 'Agentic Loop Interrupt' uses hooks in the Strands framework to intercept tool calls globally. 'Tool Context Interrupt' embeds approval logic directly within specific tools for fine-grained control. 'Remote Tool Interrupt' leverages AWS Step Functions and Amazon SNS to send approval requests asynchronously to external supervisors without blocking the agent. Finally, 'MCP Elicitation' utilizes the new Model Context Protocol feature for real-time, interactive approval prompts using server-sent events. All patterns are built on Amazon Bedrock AgentCore Runtime for serverless scalability and include open-source code examples on GitHub, demonstrating a practical path to deploying auditable, agentic automation in high-stakes environments.