Recursive Advisors: Self-Improving Agentic Systems with Spring AI
Christian Tzolov - Broadcom
AI agents are systems dedicated to the art of context window curation—relentless loops of assembling context, prompting the model, observing results, and re-assembling for the next step.
This talk explores how Spring AI provides an elegant framework for building self-improving agentic systems.
After briefly covering the essentials—ChatClient, prompt engineering, conversation memory, RAG for context enrichment, Tools for external services, and MCP for standardized integration—we’ll focus on useful agentic patterns to turn a reactive AI assistant into something that can reason, plan, and act.
We’ll explore patterns such as Agent Skills for modular, LLM-agnostic capabilities loaded on demand; Todo to prevent “lost in the middle” failures with structured planning; Subagent to delegate to specialized agents with isolated context windows; A2A and ACP Protocols for interoperable agents crossing system boundaries; LLM-as-a-Judge for automated response evaluation; and Tool Search Tool for dynamic tool discovery
You’ll leave with a clear understanding of how to integrate generative AI into Spring applications, from your first prompt to a complete agentic architecture.