Mastering Agentic AI Systems: Architect Production-Ready Autonomous Multi-Agent Workflows with MCP, A2A, LangChain, LangGraph, RAG 2.0, Open-Source LLMs, Docker, Kubernetes, and Enterprise Governance - Softcover
Synopsis
Mastering Agentic AI Systems delivers the ultimate engineering playbook for building autonomous multi-agent systems that reason, collaborate, negotiate, and self-orchestrate across enterprise-grade workflows. Leveraging open-source LLMs, Model Context Protocol (MCP), Agent-to-Agent (A2A) communication, LangChain, LangGraph, RAG 2.0, Docker, Kubernetes, and production governance frameworks, this hands-on guide transforms developers into agentic architects capable of deploying scalable, secure, and explainable autonomous agents today.
Packed with executable code, deployment blueprints, security patterns, monitoring dashboards, and real-world case studies, every chapter equips AI engineers, ML architects, and technical leaders with production-ready solutions for intelligent assistants, SaaS orchestration platforms, and mission-critical agent networks. Master dynamic retrieval, stateful reasoning, distributed decision-making, and cloud-native scaling—without vendor lock-in.
What You’ll Learn
- Design full-stack agentic architectures with MCP-driven context persistence and A2A negotiation protocols
- Orchestrate complex multi-step reasoning using LangChain and LangGraph state machines
- Implement adaptive RAG 2.0 pipelines that evolve retrieval strategies based on reasoning gaps
- Containerize and scale agent fleets with Docker, Kubernetes, Helm, and service meshes
- Enforce enterprise governance, RBAC, audit trails, and anomaly detection for agentic systems
- Build fault-tolerant, self-healing agent networks with observability, tracing, and chaos engineering
AI Engineers, ML Architects, DevOps Specialists, CTOs, and Researchers ready to ship autonomous agents into production.
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Mastering Agentic AI Systems: Architect Production-Ready Autonomous Multi-Agent Workflows with MCP, A2A, LangChain, LangGraph, RAG 2.0, Open-Source LL
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Paperback. Condition: new. Paperback. Mastering Agentic AI Systems delivers the ultimate engineering playbook for building autonomous multi-agent systems that reason, collaborate, negotiate, and self-orchestrate across enterprise-grade workflows. Leveraging open-source LLMs, Model Context Protocol (MCP), Agent-to-Agent (A2A) communication, LangChain, LangGraph, RAG 2.0, Docker, Kubernetes, and production governance frameworks, this hands-on guide transforms developers into agentic architects capable of deploying scalable, secure, and explainable autonomous agents today. Packed with executable code, deployment blueprints, security patterns, monitoring dashboards, and real-world case studies, every chapter equips AI engineers, ML architects, and technical leaders with production-ready solutions for intelligent assistants, SaaS orchestration platforms, and mission-critical agent networks. Master dynamic retrieval, stateful reasoning, distributed decision-making, and cloud-native scaling-without vendor lock-in.What You'll LearnDesign full-stack agentic architectures with MCP-driven context persistence and A2A negotiation protocolsOrchestrate complex multi-step reasoning using LangChain and LangGraph state machinesImplement adaptive RAG 2.0 pipelines that evolve retrieval strategies based on reasoning gapsContainerize and scale agent fleets with Docker, Kubernetes, Helm, and service meshesEnforce enterprise governance, RBAC, audit trails, and anomaly detection for agentic systemsBuild fault-tolerant, self-healing agent networks with observability, tracing, and chaos engineeringWho This Book Is ForAI Engineers, ML Architects, DevOps Specialists, CTOs, and Researchers ready to ship autonomous agents into production. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798273534353
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Paperback. Condition: new. Paperback. Mastering Agentic AI Systems delivers the ultimate engineering playbook for building autonomous multi-agent systems that reason, collaborate, negotiate, and self-orchestrate across enterprise-grade workflows. Leveraging open-source LLMs, Model Context Protocol (MCP), Agent-to-Agent (A2A) communication, LangChain, LangGraph, RAG 2.0, Docker, Kubernetes, and production governance frameworks, this hands-on guide transforms developers into agentic architects capable of deploying scalable, secure, and explainable autonomous agents today. Packed with executable code, deployment blueprints, security patterns, monitoring dashboards, and real-world case studies, every chapter equips AI engineers, ML architects, and technical leaders with production-ready solutions for intelligent assistants, SaaS orchestration platforms, and mission-critical agent networks. Master dynamic retrieval, stateful reasoning, distributed decision-making, and cloud-native scaling-without vendor lock-in.What You'll LearnDesign full-stack agentic architectures with MCP-driven context persistence and A2A negotiation protocolsOrchestrate complex multi-step reasoning using LangChain and LangGraph state machinesImplement adaptive RAG 2.0 pipelines that evolve retrieval strategies based on reasoning gapsContainerize and scale agent fleets with Docker, Kubernetes, Helm, and service meshesEnforce enterprise governance, RBAC, audit trails, and anomaly detection for agentic systemsBuild fault-tolerant, self-healing agent networks with observability, tracing, and chaos engineeringWho This Book Is ForAI Engineers, ML Architects, DevOps Specialists, CTOs, and Researchers ready to ship autonomous agents into production. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798273534353
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