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Building Agentic AI with Python + LangGraph + MCP + RAG 2.0: Master Production-Ready Intelligent Agents with Advanced Reasoning, Modular Context, and Enhanced Retrieval
Unlike traditional AI pipelines, agentic systems operate through structured control flows, context-aware memory modules, tool-driven execution, and dynamic retrieval patterns. This book gives you the full architecture and practical code to implement agent frameworks that leverage Python, LangGraph, Model Context Protocol (MCP), and RAG 2.0 retrieval.
You will build modular agents capable of managing complex tasks, orchestrating tools, injecting long-term memory, enforcing safety boundaries, and producing grounded, auditable outputs. From single-agent flows to advanced multi-agent collaborations with role separation, message passing, and shared memory coordination, every chapter moves you closer to architecting production-ready agentic intelligence.
Whether you’re developing research assistants, writing systems, support agents, compliance tools, or autonomous workflows, this book delivers fully executable, real-world implementations that scale from prototype to deployment.
What you’ll Master Architecting LangGraph-based reasoning pipelines with stateful control flowsSecure tool integration with validated I/O, retries, and fallback controlBuilding modular MCP context injectors for memory, user profiling, and dynamic context routingAdvanced RAG 2.0 retrieval pipelines with metadata filtering, hybrid ranking, and source traceabilityImplementing role-based multi-agent collaboration through shared memory and message-passingDesigning safety critics, constitutional reasoning layers, and guardrails for reliable output controlFull deployment pipelines with FastAPI, Docker, CI/CD, observability, and real-time metrics
This is not a conceptual overview—it’s a deeply practical engineering guide packed with complete code, architectural patterns, deployment scripts, and best practices grounded in real-world production requirements.
If you’re serious about mastering next-generation agentic AI development —and building systems that actually work in production— this is your definitive blueprint.
You will build modular agents capable of managing complex tasks, orchestrating tools, injecting long-term memory, enforcing safety boundaries, and producing grounded, auditable outputs. From single-agent flows to advanced multi-agent collaborations with role separation, message passing, and shared memory coordination, every chapter moves you closer to architecting production-ready agentic intelligence.
Whether you’re developing research assistants, writing systems, support agents, compliance tools, or autonomous workflows, this book delivers fully executable, real-world implementations that scale from prototype to deployment.
What you’ll Master Architecting LangGraph-based reasoning pipelines with stateful control flowsSecure tool integration with validated I/O, retries, and fallback controlBuilding modular MCP context injectors for memory, user profiling, and dynamic context routingAdvanced RAG 2.0 retrieval pipelines with metadata filtering, hybrid ranking, and source traceabilityImplementing role-based multi-agent collaboration through shared memory and message-passingDesigning safety critics, constitutional reasoning layers, and guardrails for reliable output controlFull deployment pipelines with FastAPI, Docker, CI/CD, observability, and real-time metrics
This is not a conceptual overview—it’s a deeply practical engineering guide packed with complete code, architectural patterns, deployment scripts, and best practices grounded in real-world production requirements.
If you’re serious about mastering next-generation agentic AI development —and building systems that actually work in production— this is your definitive blueprint.
284 pages, Kindle Edition
Published June 12, 2025
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