Agent Loops, Decoded: Fundamentals to Agentic Patterns in Spring AI

At their core, AI agents are context orchestrators — gathering information, querying models, evaluating outputs, and adapting their approach. This live-coded session takes you from Spring AI fundamentals to agentic patterns.
We start with the fundamentals: ChatClient essentials, context engineering, and session memory. We cover how Advisors extend the LLM execution path, including Recursive Advisors implementing the Agent Loop — the loop behind every autonomous agent — then enrich context with RAG and connect to external services via Tool Calling and MCP.

From there, we build up the agentic patterns Tool Calling and Advisors make possible:

• Tool Search Tool — dynamic tool discovery for agents with hundreds of tools
• LLM-as-a-Judge — automated response evaluation in an evaluator-optimizer loop
• Explainable Agents — augmenting tool calls with the model's reasoning for debuggability
• Agent Skills — LLM-agnostic capabilities, loaded on demand
• Todo / structured planning — preventing "lost in the middle" failures
• AskUserQuestion — gathering requirements before acting
• Subagent Orchestration — delegating to specialized agents with isolated context
• MCP, A2A, and ACP — securing tool access, building rich MCP Apps, and connecting agents to other agents and clients

You'll leave understanding how a few building blocks compose into any agentic pattern, with an early preview of what's next: durable, crash-recoverable agentic workflows in Spring AI 2.1.