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Build autonomous AI agents with Amazon Bedrock and Spring AI

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Build autonomous AI agents with Amazon Bedrock and Spring AI

Java teams have established JVM tooling, continuous integration and continuous delivery (CI/CD) pipelines, and operational patterns, yet taking AI agents from prototype to production is hard: agent frameworks do not solve scalability, governance, security, and operational concerns by default. Amazon Bedrock and Spring AI close those gaps, so Java teams can build autonomous AI agents that plan, call tools and APIs, and maintain context across multi-turn conversations. Agentic AI is moving beyond prompt-response interactions toward autonomous systems that execute complex multi-step tasks.

Java remains one of the widely used programming languages in enterprise software. Teams have existing CI/CD pipelines, operational expertise, and established patterns built around the JVM. Spring AI brings AI capabilities to the JVM through familiar Spring patterns: auto-configuration, dependency injection, and composable advisors. Developers can build AI agents using the same idioms they use for REST APIs, security, and data access.

Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. With AgentCore, you can build, deploy, and operate AI agents at scale. It is framework-agnostic and model-agnostic: you bring your preferred agent framework and foundation model (FM). With AgentCore, you can access the production infrastructure you need: managed runtime, memory, browser automation, code execution, and identity management.

In this post, we show you how to build autonomous AI agents in Java using Spring AI and Amazon Bedrock with spring-ai-agentcore . We explore the key implementation patterns for two systems. The first is a single-agent travel assistant that demonstrates tool calling and memory.


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