Enterprise Java applications have traditionally been designed for humans and other software systems to consume through REST APIs, messaging queues, and microservices. With the rise of agentic AI, autonomous AI agents are emerging as a completely new category of consumer. But simply exposing an endpoint to an LLM does not make an enterprise application agent-ready.
This practical, demo-driven session explores how existing Java and Spring Boot applications can evolve into systems that AI agents can safely discover, understand, and orchestrate. We will start with familiar Java APIs and progressively transform them into agent-accessible capabilities using tool calling, structured schemas, Spring AI, and the Model Context Protocol (MCP).
We will tackle the critical engineering challenges that arise when non-deterministic agents interact with mission-critical enterprise systems: authentication and authorization, tool-level permissions, guarding destructive operations, idempotency, retries, and end-to-end observability.
Live Demo & Practical Architecture
Approximately 40–50% of the session is dedicated to live code demonstrations using a Spring Boot application with realistic enterprise customer, order, and payment services:
- Progressive Agentification: Evolving classic Spring Boot
@RestControllerservices into agent tools. - Multi-Step Agent Workflows: Demonstrating
User request → AI Agent → Java Tools → Business Services → Database → Agent Verification → Response. - Production Guardrails: Implementing fine-grained authorization, transactional boundaries, and structured error handling for agent operations.
Key Takeaways
- Agent-Ready Design: Learn how to evaluate existing Java APIs and transform them into discoverable, well-typed agent tools without rewriting core business logic.
- Spring AI & MCP in Action: Discover how to bridge enterprise Spring Boot applications with LLMs using Spring AI tool registration and the Model Context Protocol.
- Enterprise Safety & Control: Master patterns for managing idempotency, sensitive operations, timeouts, role-based tool execution, and observability for autonomous agents.
- From Prototype to Production: Walk away with architectural blueprints and code patterns for integrating agentic capabilities into production Java ecosystems.
Target Audience
- Java and Spring Boot developers, backend engineers, and architects looking to make enterprise applications accessible to AI agents.
- Engineering teams evaluating agentic AI patterns, tool calling, and MCP within the Java ecosystem.
Prerequisites
- Proficiency in Java and foundational familiarity with Spring Boot and REST APIs.
- No prior experience with Spring AI or LLM application development is required.
