agentcore-samples: what it is, what problem it solves & why it's gaining traction

What it solves

Amazon Bedrock AgentCore is designed to remove the "undifferentiated heavy lifting" of building and managing the infrastructure required to run AI agents at scale. It allows developers to deploy production-ready agents without having to rewrite their code, regardless of which agent framework (such as CrewAI, LangGraph, or LlamaIndex) or LLM they are using.

How it works

AgentCore provides a framework-agnostic and model-agnostic infrastructure layer. It offers a secure, serverless runtime for deploying agents and tools, along with a set of managed capabilities that can be added to an agent via a CLI tool. These capabilities include:

• Gateway: Converts APIs and Lambda functions into MCP-compatible tools.
• Identity: Manages agent identity and access across AWS and third-party applications.
• Memory: Provides managed memory infrastructure for personalized experiences.
• Tools: Includes built-in tools like a Code Interpreter, Browser Tool, and Web Search Tool.
• Observability: Uses OpenTelemetry to trace, debug, and monitor performance.
• Evaluation: Provides both built-in and custom evaluators for on-demand and online evaluation.
• Policy: Implements fine-grained access control using Cedar policies.

Who it’s for

Developers and organizations building agentic AI applications who want to move from prototyping to production-ready deployment on AWS, while maintaining flexibility in their choice of frameworks and models.

Highlights

• Framework Agnostic: Supports multiple frameworks like Strands Agents, CrewAI, LangGraph, and LlamaIndex.
• Model Agnostic: Works with any Large Language Model.
• Serverless Runtime: Secure, serverless deployment for agents and tools.
• CLI-Driven Workflow: Streamlined project creation, local development, and deployment via the agentcore CLI.
• Infrastructure as Code: Provides templates for CloudFormation, AWS CDK, and Terraform.