Accelerating Life Sciences Innovation with Agentic AI on AWS
AWS developed a Healthcare and Life Sciences Agentic AI toolkit on Amazon Bedrock with starter AI agents for research, clinical development, and commercial use cases, accelerating innovation across the life sciences value chain. The toolkit enables multi-agent collaboration and secure orchestration within customer VPCs, allowing customization and integration with AWS Lambda and enterprise APIs. It addresses challenges like bridging technical-functional gaps and strict data governance while enabling fast co-development, iteration, and scaling of AI workflows. Use cases include biomarker discovery, clinical trial protocol design, and competitive intelligence, improving operational efficiency and collaboration. Advanced capabilities include supervisor agents, monitoring performance metrics, and LLM-based output evaluation for continuous improvement.
- Industry
- Healthcare
- Location
- United States
- Published
- May 2025
Reported outcomes
Strategic outcomes
Primary read
Use case focus
Showing 3 of 3
- 1Agent Workflow
- 2AI Workflow Automation
- 3Healthcare AI
- Life sciences organizations face complex, time-consuming workflows in research, clinical development, and commercial domains.
- There is a knowledge gap between technical and functional leaders in designing impactful AI agent solutions.
- Strict data governance and security policies require controlled, auditable AI actions integrated with enterprise systems.
- AWS's open-source Healthcare and Life Sciences Agentic AI toolkit on Amazon Bedrock offers starter and supervisor agents tailored for life sciences.
- The toolkit supports secure multi-agent orchestration in customer VPCs with customization, integration with AWS Lambda, and external APIs.
- Developers use a UI to co-develop, test, and demonstrate AI workflows collaboratively, bridging IT and business stakeholders.
- Evaluation tools provide metrics on goal accuracy and facilitate iterative agent improvements with LLM-based judging.
- Accelerated research and clinical development with automated AI agents handling complex tasks.
- Reduced AI development time via reusable pre-built agents and multi-agent orchestration features.
- Ensured compliance with data governance while empowering teams to rapidly build and scale AI solutions.
- Improved decision-making and innovation throughput across life sciences workflows.
Architecture
The architecture includes multiple healthcare and life sciences starter and supervisor agents orchestrated with Amazon Bedrock, integrating with AWS Lambda and external APIs within a secure VPC environment to manage workflows and data securely.
Sources & evidence1
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