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Debugging Intelligent Agents: Logs, Traces, and LLM Thinking Errors Explained
Debugging Intelligent Agents is a practical and comprehensive guide to understanding, tracing, and fixing the hidden failures inside LLM-powered agents. It teaches developers how to implement structured logging, trace execution paths, diagnose reasoning errors, and evaluate agent behavior systematically. Whether you're developing a production-grade autonomous agent or experimenting with a multi-step reasoning assistant, this book equips you with the tools to make your systems transparent, accountable, and trustworthy.
What to learnDetailed explanations of common failure modes in modern agentsFramework-agnostic strategies for structured logging and tracingTechniques for capturing agent memory, tool usage, and intermediate thoughtsBest practices for evaluating reasoning quality and detecting prompt driftReal-time introspection and debugging interface designFull code examples for building traceable and testable pipelinesGuidance on building architectures with observability and evaluation at the coreEach chapter walks through practical patterns, illustrated with code samples and workflows you can adapt to any agent framework—including LangChain, Semantic Kernel, CrewAI, or custom stacks.
This book is for software developers, AI engineers, prompt designers, and research practitioners building complex agent systems. If you’ve ever deployed an agent that silently failed, hallucinated a result, or made a flawed decision—and you couldn’t explain why—this book is for you. Readers should be comfortable with Python and LLM APIs, and have a basic understanding of how agents plan, reason, and act across tasks.
If you want to stop guessing and start understanding how your agents think—this book is your blueprint. Don’t settle for black-box AI. Build systems you can trace, test, and trust. Equip yourself with the debugging mindset and tools needed to lead in the future of intelligent systems. Start reading Debugging Intelligent Agents today—and transform every failure into a learning opportunity.
What to learnDetailed explanations of common failure modes in modern agentsFramework-agnostic strategies for structured logging and tracingTechniques for capturing agent memory, tool usage, and intermediate thoughtsBest practices for evaluating reasoning quality and detecting prompt driftReal-time introspection and debugging interface designFull code examples for building traceable and testable pipelinesGuidance on building architectures with observability and evaluation at the coreEach chapter walks through practical patterns, illustrated with code samples and workflows you can adapt to any agent framework—including LangChain, Semantic Kernel, CrewAI, or custom stacks.
This book is for software developers, AI engineers, prompt designers, and research practitioners building complex agent systems. If you’ve ever deployed an agent that silently failed, hallucinated a result, or made a flawed decision—and you couldn’t explain why—this book is for you. Readers should be comfortable with Python and LLM APIs, and have a basic understanding of how agents plan, reason, and act across tasks.
If you want to stop guessing and start understanding how your agents think—this book is your blueprint. Don’t settle for black-box AI. Build systems you can trace, test, and trust. Equip yourself with the debugging mindset and tools needed to lead in the future of intelligent systems. Start reading Debugging Intelligent Agents today—and transform every failure into a learning opportunity.
253 pages, Kindle Edition
Published July 8, 2025
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