Overview

AI Agents and MCP: Context Engineering in Practice Boost Accuracy, Streamline Multi-Step AI Tasks, and Harness Dynamic Tool Integration for Real-World Results Are your AI workflows struggling with inconsistencies, missed steps, or tool misalignments? Do multi-step tasks across agents feel unpredictable or error-prone? Modern AI requires more than raw computation-it demands context, coordination, and intelligent tool integration to operate reliably in real-world environments. AI Agents and MCP: Context Engineering in Practice offers a clear, actionable blueprint for building context-aware, multi-agent AI systems that perform with accuracy, efficiency, and resilience. This book teaches you how to structure memory, manage distributed context, and integrate dynamic tools using Model Context Protocol (MCP) to ensure consistent results across complex workflows. Inside, you will learn how to: Design context-rich memory architectures for single and multi-agent systems Coordinate AI agents for multi-step, high-volume workflows Integrate APIs and external tools dynamically, minimizing errors and redundancies Prevent common pitfalls like context bloat, hallucinations, and misaligned tool execution Implement enterprise-ready governance, logging, and security measures Apply practical projects and real-world case studies to reinforce learning Whether you are an AI engineer, data scientist, or enterprise architect, this book equips you with the skills, templates, and best practices to scale AI agents confidently while maintaining accuracy, auditability, and operational efficiency. Step beyond theory-learn how to build AI systems that are reliable, adaptable, and ready for real-world challenges.

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