Build Autonomous AI Agents in Java With Spring and Amazon

Enterprise software developers using Java often face challenges when moving artificial intelligence projects from simple prototypes into production. Standard agent frameworks rarely solve issues related to security, governance, and scaling by default. New integrations combining Amazon Bedrock and Spring AI aim to bridge these gaps for developers.

Spring AI brings familiar programming patterns to Java applications, such as auto-configuration and dependency injection. Meanwhile, Amazon Bedrock AgentCore provides production infrastructure including managed runtimes, identity management, and memory. Together, they allow Java teams to build autonomous systems that handle complex tasks using tools and APIs.

Understanding Autonomous AI Agents

Standard chat applications generate text responses based on prompts, but they cannot decide what steps to take next or remember past interactions. An autonomous agent combines three core elements: planning, tool use, and memory. The planning phase breaks a goal down into prioritized steps. Tool use lets the model invoke external functions or databases. Memory preserves context across short-term conversations and long-term sessions.

Sample Architectures for Developers

The implementation features two primary systems for engineering teams. The first is a single-agent travel assistant designed to demonstrate tool calling and memory management. The second is a multi-agent intelligent document processing system that uses five specialist agents, a rules engine, and an alerting service to handle complex document workflows at scale.

• Single-agent travel assistant for basic tool integration and streaming.
• Multi-agent document processing for handling specialized enterprise tasks.
• Shared infrastructure utilizing container images and modular provisioning.