Debugging Claude AI Agents: Loops, Bad Tool Calls, Fixes
Field guide to debugging Claude agents: why they loop, pick wrong tools, and hallucinate arguments — with concrete traces and fixes that hold.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Field guide to debugging Claude agents: why they loop, pick wrong tools, and hallucinate arguments — with concrete traces and fixes that hold.
Learn what to put in a Claude agent's context and what to leave out, and why: working-set thinking, compression, and system prompts that grant bounded agency.
Connect tools and MCP servers to a Claude agent the right way: auth at the boundary, strict schemas, structured errors, and idempotency for safe writes.
Code-level Claude agent patterns: layer prompts, design intent-shaped tools, budget context, enforce output contracts, and externalize know-how into Skills.
Follow a concrete walkthrough to build a working Claude agent: define tools, run the loop, handle results, add a scratchpad, and test against real inputs.
See how agentic AI architecture fits together with Claude: the model loop, context assembler, tool layer, memory, and orchestration explained end to end.
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