Have you ever asked an AI agent to perform a complex multi-step task, only to find it skipped a critical step halfway through? You're not alone.

Research shows that LLMs struggle with "lost in the middle" failures—forgetting tasks buried in long contexts. When your agent juggles file edits, test execution, and documentation updates, important steps can silently disappear. One solution, inspired by Claude Code, is to make planning explicit and observable with the help of a dedicated TodoWrite tool. The result: agents that never skip steps and workflows you can observe in real-time.

This is Part 3 of our Spring AI Agentic Patterns series. We've covered Agent Skills for modular capabilities and AskUserQuestionTool for interactive workflows. Now we explore how TodoWriteTool…

Traditional AI interactions follow a common pattern: you provide a prompt, the AI makes assumptions, and produces a response. When those assumptions don't match your needs, you're left iterating through corrections. Each assumption creates rework—wasting time and context.

What if your AI agent could ask you clarifying questions before providing answers?

The AskUserQuestionTool addresses this. It allows the AI agent to ask clarifying questions before answering, gathers requirements interactively, and creates a specification aligned with your actual needs from the start.

Spring AI's…

Agent Skills are modular folders of instructions, scripts, and resources that AI agents can discover and load on demand. Instead of hardcoding knowledge into prompts or creating specialized tools for every task, skills provide a flexible way to extend agent capabilities.

Spring AI's implementation brings Agent Skills to the Java ecosystem, ensuring LLM portability—define your skills once and use them with OpenAI, Anthropic, Google Gemini, or any other supported model.

This is the first post in our Spring AI Agentic Patterns series. This series explores the spring-ai-agent-utils toolkit—an extensive set of agentic patterns for Spring AI, inspired by Claude Code. We'll cover Agent Skills (this post), followed by Task Management, AskUserQuestion for interactive…

When building AI agents with tool calling capabilities, developers often need insights into why an LLM chose a particular tool—not just which tool it selected. Understanding the model's reasoning process is important for debugging, observability, and building trustworthy AI systems.

Spring AI now (2.0.0-SNAPSHOT/1.1.3-SNAPSHOT) includes the Tool Argument Augmenter feature that enables dynamic augmentation of tool input schemas with additional arguments before sending tool definitions to the LLM. This allows AI applications to capture extra information from the model—such as reasoning, inner…

As AI agents connect to more services—Slack, GitHub, Jira, MCP servers—tool libraries grow rapidly. A typical multi-server setup can easily have 50+ tools consuming 55,000+ tokens before any conversation starts. Worse, tool selection accuracy degrades when models face 30+ similarly-named tools.

The Tool Search Tool pattern, pioneered by Anthropic, addresses this: instead of loading all tool definitions upfront, the model discovers tools on-demand. It receives only a search tool initially, queries for capabilities when needed, and gets relevant tool definitions expanded into context. This achieves significant token savings while…

JSON is the go-to format for LLM tool responses, but recent discussions around alternative formats like TOON (Token-Oriented Object Notation) claim potential benefits in token efficiency and performance. While the debate continues—with critical analyses pointing to context-dependent results—the question is: how to experiment with these formats in your own Spring AI applications?

This article demonstrates how to configure Spring AI to convert tool responses between JSON, TOON, XML, CSV, and YAML, enabling you to decide what works best for your specific use case.

Spring AI Tool Calling: A Quick…

The challenge of evaluating Large Language Model (LLM) outputs is critical for notoriously non-deterministic AI applications, especially as they move into production.

Traditional metrics like ROUGE and BLEU fall short when assessing the nuanced, contextual responses that modern LLMs produce. Human evaluation, while accurate, is expensive, slow, and doesn't scale.

Enter LLM-as-a-Judge - a powerful technique that uses LLMs themselves to evaluate the quality of AI-generated content. Research shows that sophisticated judge models can align with human judgment up to 85%, which is actually higher…

The Spring AI ChatClient offers a fluent API for communicating with an AI model. The fluent API provides methods for building the constituent parts of a prompt that gets passed to the AI model as input.

Advisors are a key part of the fluent API that intercept, modify, and enhance AI-driven interactions. The key benefits include encapsulating common Generative AI patterns, transforming data sent to and from Large Language Models (LLMs), and providing portability across various models and use cases.

Advisors process ChatClientRequest and ChatClientResponse objects. The framework chains advisors by their getOrder() values (lower values execute first), with the final…

The Model Context Protocol (MCP) standardizes how AI applications interact with external tools and resources. Spring joined the MCP ecosystem early as a key contributor, helping to develop and maintain the official MCP Java SDK that serves as the foundation for Java-based MCP implementations. Building on this contribution, Spring AI has embraced MCP with comprehensive support through dedicated Boot Starters and MCP Java Annotations, making it easier than ever to build sophisticated AI-powered applications that can seamlessly connect to external systems.

This blog introduces core MCP components and demonstrates building both MCP Servers and Clients using Spring AI, showcasing basic and advanced features. The complete source code is available at: MCP Weather Example…

The Model Context Protocol (MCP) is a powerful feature in Spring AI that enables AI models to access external tools and resources through a standardized interface. One interesting capabilities of MCP is its ability to dynamically update available tools at runtime.

This blog post explores how Spring AI implements dynamic tool updates in MCP, providing flexibility and extensibility to AI-powered applications.

The related example code is available here: Dynamic Tool Update Example

Understanding the Model Context Protocol

Before diving into dynamic tool updates, let's understand what MCP is and…