AI Agents and MCP: Context Engineering in Practice: Boost Accuracy, Streamline Multi-Step AI Tasks, and Harness Dynamic Tool Integration for Real-World Results - Softcover
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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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Paperback. Zustand: new. Paperback. AI Agents and MCP: Context Engineering in PracticeBoost Accuracy, Streamline Multi-Step AI Tasks, and Harness Dynamic Tool Integration for Real-World ResultsAre 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 systemsCoordinate AI agents for multi-step, high-volume workflowsIntegrate APIs and external tools dynamically, minimizing errors and redundanciesPrevent common pitfalls like context bloat, hallucinations, and misaligned tool executionImplement enterprise-ready governance, logging, and security measuresApply practical projects and real-world case studies to reinforce learningWhether 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798198543461
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