Building Agentic AI with Python and LangGraph: A Practical Guide to Production-Ready Intelligent Systems: Master Advanced Reasoning, RAG 2.0, Modular ... LangGraph Integration and Python Automation) - Tapa blanda

Sinopsis

Unlock the power of agentic AI with this definitive, hands-on guide to architecting production-ready intelligent systems using Python, LangGraph, Model Context Protocol (MCP), and RAG 2.0. Unlike traditional AI pipelines, agentic systems leverage stateful reasoning, context-aware memory, tool-driven execution, and dynamic retrieval to deliver autonomous, scalable solutions for real-world applications. Written by Yuan Zhu, this book provides fully executable code, architectural patterns, and best practices to transform you into a master of next-generation AI development.
Dive into building modular agents capable of orchestrating complex tasks, from research assistants and customer support agents to compliance tools and autonomous workflows. You’ll learn to design LangGraph-based reasoning pipelines with stateful control flows, integrate secure tools with validated I/O and retries, and implement advanced RAG 2.0 retrieval with metadata filtering, hybrid ranking, and source traceability. Explore multi-agent collaboration with role-based systems, shared memory, and message-passing, while embedding safety critics and constitutional reasoning for reliable, ethical outputs.
This isn’t just theory it’s a practical engineering blueprint packed with complete Python code, FastAPI deployment scripts, Docker containerization, CI/CD pipelines, and real-time observability. From modular MCP context injectors for dynamic memory routing to scalable agent architectures, every chapter equips you with the tools to build production-ready AI systems that excel in performance, safety, and scalability.
What You’ll Master:

• Architect LangGraph workflows for dynamic reasoning and task orchestration
• Build secure tool integrations with robust error handling and fallback logic
• Implement MCP for context-aware memory and user profiling
• Create RAG 2.0 pipelines with metadata-driven retrieval and low-latency ranking
• Design multi-agent systems with role separation and shared memory coordination
• Embed safety guardrails and constitutional reasoning for auditable outputs
• Deploy production-grade agents with FastAPI, Docker, and real-time metrics

Whether you’re a developer, data scientist, or AI engineer, this book delivers step-by-step implementations to take your agentic AI projects from prototype to production. Build intelligent systems that reason, adapt, and scale—your blueprint for mastering agentic AI starts here.

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