Understanding why an LLM chose a specific tool can be just as important as the tool call itself. Today's Spring AI Recipe shows how to use AugmentedToolCallbackProvider to capture and inspect tool-selection reasoning from the model.

https://medium.com/@thetalkingapp/spring-ai-recipe-explaining-tool-selection-8cce493b5f94

#SpringAI #LLM #AI

I’ll be speaking at UberConf 2026 this July, including sessions on Spring AI alongside a fantastic lineup of Java, architecture, cloud, leadership, and software development topics.

Use my speaker discount code (uber26sp-cw) for $300 off registration. Attendees also receive 25 NFJS Virtual Credits (12.5 days of virtual training).

#UberConf #Java #SpringBoot #SpringAI

Spring AI’s ToolCallAdvisor unlocks something SimpleLoggerAdvisor couldn’t previously see: the full tool-calling conversation between your app and the LLM. See every request, tool negotiation, and response across all Spring AI model abstractions.

https://medium.com/@thetalkingapp/spring-ai-recipe-better-llm-request-response-logging-with-toolcalladvisor-de3028af3d46

#SpringAI #AI #LLM

Tools give agents power. Security determines who gets to use that power.

In today's Spring AI recipe, you'll secure an MCP server with an API key.

https://medium.com/@thetalkingapp/spring-ai-recipe-securing-an-mcp-server-with-an-api-key-0a4b84fdf0dc

#SpringAI #MCP #AgenticAI #Java #Security

Your agent's workflow doesn’t have to settle for its first answer. Add a simple loop and let it evaluate, revise, and improve until it gets it right.

Today's Spring AI Recipe builds upon previous recipes to add a loop to a graph-based workflow.

https://medium.com/@thetalkingapp/spring-ai-recipe-adding-a-loop-to-a-graph-based-workflow-e062040e0440

#SpringAI #AgenticAI #Java #LLM

Over the past few weeks, I’ve been publishing a growing series of Spring AI Recipes covering agents, MCP, A2A, memory, workflows, tools, and more.

There’s now a central place to find all of the recipes in one spot: https://www.habuma.com/springairecipes/

#SpringAI #Java #AgenticAI #MCP #LLM

My April 2026 AI Retrospective is up 🧵
Covering: persistent knowledge bases (wiki-over-RAG via Karpathy’s gist), hands-on time with Granola for meeting notes, evaluating Emdash & Supacode for parallel agent workflows, and the multi-repo problem that nobody’s solved well yet.
Also: two posts on AI writing consistency and MCP with Spring AI.
👉 https://robintegg.com/2026/05/06/april-2026-ai-retrospective.html
#AI #Java #SpringAI #Agents #Retrospective
April 2026 AI Retrospective

Consolidation, knowledge bases, and trying out the new wave of harness tools, a personal retrospective on April 2026.

Robin Tegg

New article: Exploring MCP with Spring AI 🧵

MCP (Model Context Protocol) is becoming a key primitive in the AI tooling landscape — giving LLMs structured access to your application via Tools, Resources, Prompts, and Apps.

Spring AI wraps the MCP Java SDK with Boot starters and annotations that feel natural to any Spring developer.

https://robintegg.com/2026/04/26/exploring-mcp-with-spring-ai.html

#Java #SpringAI #SpringBoot #MCP #AI

Exploring MCP with Spring AI

MCP gives your LLM access to external systems via your agent. Spring AI makes it straightforward to build MCP servers in Java using annotations.

Robin Tegg
Great agentic workflows aren’t just AI on autopilot—they’re a collaboration between human insight and AI execution. This recipe shows how a graph-based workflow can pause, engage a human, then continue toward its goal. #SpringAI #Java #AI #Agents #LLM

Completely autonomous agents are like unplanned road trips--flexible, but unpredictable. Graph-based workflows provide a roadmap, while still allowing decisions along the way.

In today’s Spring AI recipe you'll build a graph-based agentic workflow with Spring AI Alibaba Graph.

https://medium.com/@thetalkingapp/spring-ai-recipe-building-a-graph-based-agentic-workflow-becfae64170a

#SpringAI #AgenticAI #Java #LLM