Java developers want to build AI agents using known Spring patterns, but production deployment requires infrastructure that’s complex to implement from scratch. Amazon Bedrock AgentCore provides building blocks like managed runtime infrastructure (scalability, reliability, security, observability), short- and long-term memory, browser automation, sandboxed code execution, and evaluations. Integrating these capabilities into a Spring application currently requires writing custom controllers to fulfill AgentCore Runtime contract, handling Server-Side Events (SSE) streaming, implementing health checks, managing rate limiting, and wiring up Spring advisors, memory repositories, and tool definitions. This is weeks of infrastructure work before writing any AI Agent logic.

With the new Spring AI AgentCore SDK, you can build production-ready AI agents and run them on the highly scalable AgentCore Runtime. The Spring AI AgentCore SDK is an open source library that brings Amazon Bedrock AgentCore capabilities into Spring AI through known patterns: annotations, auto-configuration, and composable advisors. SpringAI Builders add a dependency, annotate a method, and the SDK handles the rest.

AgentCore Runtime manages agent lifecycle and scaling with pay-per-use pricing, meaning you don’t pay for idle compute. The runtime routes incoming requests to your agent and monitors its health, but this requires your agent to follow a contract. The contract requires that the implementation exposes two endpoints. The /invocations endpoint receives requests and returns responses as either JSON or SSE streaming. The /ping health endpoint reports a Healthy or HealthyBusy status. Long-running tasks must signal that they’re busy, or the runtime might scale them down to save costs. The SDK implements this contract automatically, including async task detection that reports busy status when your agent is processing.

Beyond the contract, the SDK provides additional capabilities for production workloads like handling SSE responses with proper framing, backpressure handling, and connection lifecycle management for large responses. It also provides rate limiting, throttling requests to protect your agent from traffic spikes and limit per-user consumption. You focus is on agent logic while the SDK handles the runtime integration.

In this post, we build a production-ready AI agent starting with a chat endpoint, then adding streaming responses, conversation memory, and tools for web browsing and code execution. By the end, you will have a fully functional agent ready to deploy to AgentCore Runtime or run standalone on your existing infrastructure.