As large language models evolve, they are increasingly embedded within autonomous or semi-autonomous systems capable of reasoning, planning, and interacting with tools. Engineering LLM-Powered AI Agents explores how to design structured agent architectures that extend beyond simple text generation. This book presents engineering patterns for: Agent planning and reasoning loopsTool integration and API orchestrationMemory systems and context managementRetrieval-augmented generation (RAG)Multi-agent communication modelsGuardrails and controlled autonomyEvaluation of agent reliability and behaviorRather than presenting abstract concepts, this volume emphasizes architectural clarity and practical implementation approaches. Readers will learn how to decompose complex workflows into modular components, design robust interaction loops, and measure system behavior under varied conditions. The book is suited for AI engineers, solution architects, and developers who want to build structured, extensible agent systems grounded in responsible design principles.
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