Amazon Bedrock AgentCore Runtime Simplifies AI Agent Deployment

Organizations are increasingly excited about the potential of AI agents but often struggle to move from prototypes to production. Amazon Bedrock AgentCore Runtime addresses these challenges with a secure, serverless hosting environment designed specifically for AI agents and tools.

Key Challenges Addressed

• Framework and Model Flexibility: Developers can use different frameworks (LangGraph, CrewAI, Strands) and models (Amazon Bedrock, Claude, OpenAI, Gemini) without architectural changes. GitHub samples demonstrate this flexibility.
• Session Isolation: Each session runs in an isolated microVM, ensuring security and preventing cross-contamination.
• Embedded Identity: Supports IAM and OAuth-based authentication, enabling secure agent operations on behalf of users.
• Large Payload Support: Handles payloads up to 100 MB, ideal for processing images, documents, and other large data types.
• Asynchronous Operations: Agents can run for up to 8 hours, with tools like add_async_task and complete_async_task simplifying background task management.
• Cost Efficiency: Pay only for active CPU and memory usage, not idle time.

How It Works

1. Deployment: With just four lines of code, developers can deploy agents: python from bedrock_agentcore.runtime import BedrockAgentCoreApp app = BedrockAgentCoreApp() @app.entrypoint app.run()

2. State Management: Combines ephemeral session state with persistent storage via AgentCore Memory.

3. Security: MicroVM isolation and embedded identity ensure secure, multi-tenant operations.

Real-World Applications

• Customer Support: Agents handle 10,000+ daily queries with 70% cost savings by paying only for active CPU usage.
• Data Analysis: Financial agents process large datasets asynchronously, spiking resource usage only during intensive computations.

Conclusion

Amazon Bedrock AgentCore Runtime eliminates infrastructure complexity, enabling developers to focus on building intelligent agent experiences. For hands-on examples, explore the GitHub repo.

Authors: Shreyas Subramanian, Kosti Vasilakakis, and Vivek Bhadauria