Agentic AI with LangChain

is a comprehensive, architecture-first engineering guide for building, deploying, and operating scalable multi-agent AI systems in production environments. Written for experienced practitioners, this book goes beyond prompt engineering and single-agent workflows to address the real challenges of designing agentic systems that are reliable, controllable, observable, and cost-effective at scale.
The book examines how agent-based systems actually behave in production, focusing on system-level concerns such as coordination, autonomy boundaries, memory management, reasoning control loops, failure modes, and long-term operational stability. Using LangChain as the foundational framework, it explores how agents interact with tools, data, and each other within structured orchestration models designed for enterprise use cases.
Rather than presenting code examples or experimental demos, the book emphasizes architecture diagrams, system workflows, and engineering trade-offs. Readers learn how to design agent roles, manage inter-agent communication, orchestrate complex workflows, and integrate retrieval-augmented generation into agentic systems while maintaining accuracy, traceability, and governance.
Special attention is given to production realities, including testing agent behavior, evaluating system quality beyond task completion, implementing observability across distributed agents, controlling latency and cost, and defending against agent-specific risks such as runaway execution, prompt injection, and cascading failures. The book also covers deployment strategies, security considerations, multi-tenant scaling, and the long-term operation and evolution of agentic systems.
Designed as a definitive reference,
Agentic AI with LangChain
equips LLM engineers, AI architects, and technical leaders with the architectural patterns and system thinking required to build agentic AI platforms that move confidently from prototype to production.

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