AWS and Spring AI Join Forces to Bring Java Autonomous Agents into Production with Amazon Bedrock AgentCore
Integration via spring-ai-agentcore lets Java teams build agents that plan, call tools, and maintain context across multiple conversations using Bedrock AgentCore's managed infrastructure
Amazon Web Services has published a technical guide demonstrating how Java teams can build autonomous AI agents in production using Spring AI integrated with Amazon Bedrock AgentCore, through the open-source project spring-ai-agentcore. The goal is to solve a concrete problem: agent frameworks do not deliver scalability, governance, security, and operations by default.
Java remains one of the most widely used languages in enterprise software. Teams already have CI/CD pipelines, operational expertise, and established standards around the JVM. Spring AI brings AI capabilities to this environment through patterns familiar to the Spring ecosystem — autoconfiguration, dependency injection, and composable advisors.
What distinguishes an agent from a chatbot
A chat completion call can generate text, but it does not decide the next step, does not access external systems, and does not remember what happened yesterday. An agent combines three capabilities: planning, tool use, and memory. A RAG application has limited tool use (retrieval only) but no planning loop. The agent operates in a goal-driven loop until it completes the task or determines it cannot proceed.
The reference architecture presented has 11 stages. A client submits a document and a question. The request passes through a runtime gateway for authentication and rate limiting, then through input guardrails that filter harmful content. A router classifies the document and forwards it to a specialist agent, which plans its flow and calls tools, the Bedrock foundation model, memory, and a rules engine as needed.
Managed infrastructure and governance
Low-confidence or flagged results go to a human review queue before the grounded response returns to the client. Resilience, observability, logging, and least-privilege permissions via AWS Identity and Access Management (IAM) apply across the entire pipeline.
Amazon Bedrock AgentCore is the platform that sustains this operation at scale. It is framework- and model-agnostic: teams bring their preferred agent framework and foundation model. The platform provides the managed runtime, memory, browser automation, code execution, and identity management required for production.
Two reference systems
The guide details two implementation systems. The first is a single-agent travel assistant, which demonstrates tool calling and memory. The second is a multi-agent Intelligent Document Processing (IDP) system, which shows how specialist agents, a rules engine, and alerts work together.
For Java developers, the proposition is to use the same idioms they already apply to REST APIs, security, and data access. The sample code is available in the aws-samples repository on GitHub, and the spring-ai-agentcore project is maintained by the Spring AI community.