Accelerating Biomedical Research: Leveraging AI Agents with Amazon Bedrock and Biomni
Introduction
Unlocking the potential of AI in the biomedical research space by integrating advanced agents with robust infrastructures for enhanced data discovery and analysis.
Bridging the Complexity Gap from Prototype to Production
1. Enterprise Security for AI Agents
Addressing the security challenges in deploying AI agents while conducting sensitive biomedical research.
2. Contextual Management in Research Workflows
Ensuring continuity and context in biomedical investigations across diverse research sessions.
3. The Role of a Scalable Tool Gateway
Facilitating efficient access and management of specialized biomedical tools for enhanced research productivity.
A Comprehensive Solution Overview
Implementing a research agent leveraging specialized tools for improved biomedical analyses and outcomes.
Step 1: Creating a Gateway for the Biomni Tools
Simplifying agent access to diverse databases and applications through a centralized gateway.
Step 2: Deploying Strands Research Agent
Integrating local tools within the research agent framework for enhanced usability.
Step 3: Building Persistent Memory for Enhanced Context
Utilizing memory services to improve contextual awareness in research.
Step 4: Deploying and Managing with AgentCore Runtime
Streamlining deployment processes for effective, scalable research capabilities.
Case Studies: Agents in Action
Exploring representative use cases showcasing the versatility of AI agents in various biomedical research scenarios.
Future Directions in Biomedical Research AI
Anticipating advancements to support ongoing tool evolution and multi-agent collaborations.
Conclusion
Demonstrating the potential of AI agents in transforming biomedical research, ensuring scalability, security, and integration.
About the Authors
Profiles of the individuals driving this transformative research initiative.
Accelerating Biomedical Research with AI Agents: The Role of Biomni and Amazon Bedrock AgentCore
This post is co-authored with the Biomni group from Stanford.
Biomedical researchers are often inundated with vast amounts of information, spending roughly 90% of their time manually processing data. A striking example is Genentech’s challenge of analyzing 38 million biomedical publications alongside numerous databases like the Human Protein Atlas. The rapid growth of specialized databases and analytical tools in genomics, proteomics, and pathology means researchers must stay updated on their capabilities, sidelining the hypothesis-driven work that spurs breakthrough discoveries.
Enter AI agents. Powered by foundation models, they offer a game-changing solution by autonomously planning, executing, and adapting complex research tasks. The Biomni project developed at Stanford embodies this potential; it is a versatile biomedical AI agent that integrates over 150 specialized tools, 105 software packages, and 59 databases to facilitate sophisticated analyses like gene prioritization and drug repurposing.
The Challenge of Production-Ready AI in Biomedical Research
Deploying such agents in a production environment demands a robust infrastructure that effectively addresses the challenges of computational workflows, user access, and data security. Amazon’s Bedrock AgentCore serves as a comprehensive solution for deploying and operating these intelligent agents with enterprise-grade security and scalability.
This post details how to construct a research agent using AgentCore, leveraging access to over 30 specialized biomedical database tools from Biomni, ultimately expediting scientific discovery while ensuring robust security and scalability.
Bridging the Prototype-to-Production Gap
Moving from a localized prototype to a production-ready system accessible by various research teams presents a range of infrastructure challenges.
Security Concerns in Agent Deployment
Enterprise security issues are paramount. These involve implementing OAuth-based authentication, secure tool sharing, and thorough observability for auditing research workflows. Many promising projects never make it to production due to the complexity of fulfilling enterprise-grade requirements while retaining the specialized expertise necessary for precise biomedical analyses.
Context Management for Research Relevance
Biomedical research often unfolds across multiple conversations, requiring an enduring memory of previous analyses, experimental parameters, and research preferences. Research agents must retain contextual knowledge while ensuring isolation between different projects in a multi-tenant environment.
Scalable Tool Gateway Implementation
Creating a reusable tool gateway is vital for accommodating concurrent requests from research agents and managing authentication effectively. The gateway not only needs to facilitate tool discovery but also must manage authentication securely, making it critical for scaling research efforts.
Overview of the Solution
This article employs Strands Agents, an open-source framework, to construct a research agent that integrates local tools for PubMed searches and extends its capabilities via Biomni’s databases.
Key Components of the AgentCore Framework
- AgentCore Gateway: Centralizes access to biomolecular tools through secure endpoints with semantic search capabilities.
- AgentCore Memory: Maintains contextual awareness across research sessions.
- AgentCore Identity: Manages user authentication and tool access.
- AgentCore Runtime: Streamlines deployment, enabling scalable, managed service configurations.
- AgentCore Observability: Provides critical monitoring for auditing research processes.
Practical Steps to Set Up the Research Agent
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Create a Centralized Tool Gateway: The AgentCore Gateway service helps centralize tools into secure endpoints, making them reusable and simplifying agent access.
def create_gateway(gateway_name: str, api_spec: list) -> dict: # JWT authentication with Cognito auth_config = { "customJWTAuthorizer": { "allowedClients": [ get_ssm_parameter("/app/researchapp/agentcore/machine_client_id") ], "discoveryUrl": get_ssm_parameter("/app/researchapp/agentcore/cognito_discovery_url"), } } ... create_gateway("MyAppTemplateAgentCoreGateway", api_spec) -
Implement PubMed Tool in the Agent: Using the Strands framework to extend the agent’s capabilities for comprehensive biomedical literature search.
from agent.agent_config.tools.PubMed import PubMed @tool(name="Query_pubmed") def query_pubmed(query: str) -> str: pubmed = PubMed() result = pubmed.run(query) return result -
Add Persistent Memory: This allows the agent to maintain context across sessions by integrating with the AgentCore Memory service.
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Deploy with AgentCore Runtime: Configure and launch the agent as a managed service.
agentcore configure --entrypoint agent/main.py -er arn:aws:iam::<Account-Id>:role/<Role> --name researchapp<AgentName> -
Test Your Agent: Use automated scripts to verify the tool functionalities.
Illustrative Research Scenarios
- Drug Mechanism Analysis: Analyzing trastuzumab’s mechanism and resistance mechanisms by querying relevant biomedical databases.
- Genetic Variant Investigation: Investigating clinical significance of BRCA1 variants in breast cancer.
Scalability and Observability
As research workflows grow more unpredictable, having scalable and observable AI agents becomes crucial. Amazon Bedrock AgentCore Observability allows teams to monitor agent performance, ensuring timely issue resolution critical for maintaining research momentum.
Future Directions
The future of AI in biomedicine is promising. Further enhancements, such as integrating multi-agent collaboration and specialized research tools tailored to particular therapeutic areas, can significantly evolve research capabilities.
Conclusion
This implementation demonstrates how Amazon Bedrock AgentCore can transform biomedical research prototypes into production systems. By leveraging Biomni’s extensive toolset integrated through the AgentCore Gateway, research teams can establish a secure and scalable infrastructure. The project exemplifies how AI can catalyze breakthroughs in biomedical research, enabling researchers to focus more on hypothesis-driven work rather than data management.
To get started on building your own biomedical research agent with Biomni tools, explore our GitHub repository for complete code and documentation, and join the collaborative journey in advancing biomedical AI.
About the Authors
Hasan Poonawala, Pierre de Malliard, Necibe Ahat, and Kexin Huang are leading experts in AI solutions for healthcare, focusing on enhancing biomedical research through innovative technology.
For more insights into the implementation of AI agents in biomedical research, visit [GitHub repository link].