--- title: LLM Tool Call Reasoning Using Embabel Agentic AI Framework description: Tool-call reasoning improves observability of AI agents by capturing the rationale behind LLM tool selection. This tutorial compares two approaches: Spring... canonical: https://daily.dev/posts/llm-tool-call-reasoning-using-embabel-agentic-ai-framework-uucsie3do twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: LLM Tool Call Reasoning Using Embabel Agentic AI Framework | daily.dev og:description: Tool-call reasoning improves observability of AI agents by capturing the rationale behind LLM tool selection. This tutorial compares two approaches: Spring... og:url: https://daily.dev/posts/llm-tool-call-reasoning-using-embabel-agentic-ai-framework-uucsie3do og:image: https://api.daily.dev/og/posts/uUcsIe3Do.png og:image:alt: LLM Tool Call Reasoning Using Embabel Agentic AI Framework og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # LLM Tool Call Reasoning Using Embabel Agentic AI Framework **[Baeldung](https://daily.dev/sources/baeldung)** · 13 min read · 2 upvotes · 0 comments ## Summary Tool-call reasoning improves observability of AI agents by capturing the rationale behind LLM tool selection. This tutorial compares two approaches: Spring AI's Tool Argument Augmenter (schema augmentation) and a simpler prompt-based protocol. It then demonstrates how the Embabel Agentic AI framework's 'thinking' mode provides a clean, prompt-based solution using ThinkingResponse and ThinkingBlocks. A parking recommendation use case illustrates how to define tools with @LlmTool, configure a two-phase system prompt for structured reasoning, and extract per-tool confidence scores and decision rationale from the LLM's output without modifying tool implementations or introducing synthetic schemas. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Explainable AI Agents: Capture LLM Tool Call Reasoning with Spring AI](https://daily.dev/posts/explainable-ai-agents-capture-llm-tool-call-reasoning-with-spring-ai-uaxu9alki) · Baeldung · 3 upvotes · 0 comments - [Explainable AI Agents: Capture LLM Tool Call Reasoning with Spring AI](https://daily.dev/posts/explainable-ai-agents-capture-llm-tool-call-reasoning-with-spring-ai-jxsstkrzq) · Spring · 2 upvotes · 0 comments - [LLM Streaming in the Embabel Agentic AI Framework](https://daily.dev/posts/llm-streaming-in-the-embabel-agentic-ai-framework-sucft0aas) · Baeldung · 3 upvotes · 0 comments --- Tags: [#java](https://daily.dev/tags/java), [#ai-agents](https://daily.dev/tags/ai-agents), [#spring-ai](https://daily.dev/tags/spring-ai) [View this post on daily.dev](https://daily.dev/posts/llm-tool-call-reasoning-using-embabel-agentic-ai-framework-uucsie3do) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"LLM Tool Call Reasoning Using Embabel Agentic AI Framework","url":"https://daily.dev/posts/llm-tool-call-reasoning-using-embabel-agentic-ai-framework-uucsie3do","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/llm-tool-call-reasoning-using-embabel-agentic-ai-framework-uucsie3do"},"datePublished":"2026-07-12T10:08:52.401Z","dateModified":"2026-07-12T10:09:35.211Z","description":"Tool-call reasoning improves observability of AI agents by capturing the rationale behind LLM tool selection. 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