Java developers can build autonomous AI agents with Spring AI and Amazon Bedrock

Moving beyond simple chat

Most current AI interactions are limited to simple prompt-response cycles. While a chat completion can generate text, it lacks the ability to decide on next steps, interact with external systems, or remember past interactions independently. This creates a significant hurdle for companies trying to move AI from a prototype to a production-ready tool.

Autonomous agents solve this by combining three specific capabilities: planning, tool use, and memory. Planning allows a model to break a high-level goal into prioritized steps and revise that plan as new information arrives. Tool use enables the model to invoke external functions like APIs, databases, or code interpreters to act on the world.

Memory ensures that context carries across both short-term turns and long-term sessions. This allows each decision the agent makes to build on previous interactions. Unlike Retrieval Augmented Generation (RAG) applications, which have limited tool use for retrieval, an agent operates in a goal-directed loop until a task is finished.

Java remains a primary language for enterprise software, but taking AI agents to production often lacks standard governance and scalability. Spring AI addresses this by bringing AI capabilities to the JVM through familiar patterns like auto-configuration and dependency injection. Developers can build agents using the same idioms they already use for REST APIs, security, and data access.

Amazon Bedrock AgentCore serves as the infrastructure layer for these agents. It is a framework-agnostic platform that allows teams to bring their own foundation models and agent frameworks. AgentCore provides the managed runtime, memory, browser automation, and identity management required for production environments. This allows Java teams to focus on logic rather than infrastructure.

The integration works by using the spring-ai-agentcore SDK to bridge the application layer and the infrastructure. Developers can use annotations like @AgentCoreInvocation to automatically wire up required endpoints. This handles JSON serialization, manages streaming responses via Server-Sent Events, and reports health status to the runtime automatically.

Core agentic patterns

The solution demonstrates two primary systems to show how these tools work in practice. The first is a single-agent travel assistant that handles tool calling for weather lookups and itinerary searches. It uses streaming responses and conversation memory to maintain a fluid user experience while resolving tool invocations locally within the process.

The second is a more complex multi-agent intelligent document processing system. This system uses a ClassifierAgent to route documents to five different specialist agents, such as a Lawyer or Tax agent. Each specialist extracts structured JSON data using the multimodal Converse API of Amazon Bedrock and provides a confidence score for the results.

To coordinate these agents, the system uses three specific patterns. A router dispatches requests based on document type, while an evaluator uses a rules engine to check the findings against business rules. Additionally, a parallel fan-out can be used to send a single request to multiple specialists concurrently, bounding the total time by the slowest single call.

Production-ready guardrails

Deploying autonomous agents requires significant oversight to ensure safety and reliability. The architecture includes an 11-stage pipeline that moves a request from the client to a grounded answer. This process includes a runtime gateway for authentication, input guardrails to screen for harmful content, and a router to classify the request for the correct specialist.

The system also incorporates a human review queue for low-confidence or flagged results. This ensures that the AI does not act autonomously on sensitive data without oversight. Resilience, observability, and logging are applied as cross-cutting concerns across every stage of the pipeline to maintain system health.

Security is managed through least-privilege AWS Identity and Access Management (IAM) policies. By combining these enterprise-grade features with the Spring AI framework, Java teams can build systems that are both powerful and compliant. This setup allows for high-scale operations while maintaining the strict controls required in corporate environments.

Implementation requirements

To build these systems, developers need a specific set of modern tools and versions. The requirements include Java JDK 21, Spring Boot 4.1, and Maven 3.9+. For the frontend and infrastructure, the setup requires Node.js 18+, Terraform 1.5+, and Docker with Buildx for multi-architecture builds.

The architecture is optimized for performance using ARM64 container images for AWS Graviton. Developers can use the @Tool annotation to define methods that the model can invoke during its reasoning process. For example, a method can be annotated to look up a weather forecast, and the model will decide when to call it based on the user's prompt.

For structured data, the system maps model outputs directly to Java records. This ensures that the information extracted by the AI is usable by other parts of the software. While behavior can differ between different model families, the Spring AI framework provides a consistent way to handle these interactions across various providers.