Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Context Engineering for AI Agents: Build Reliable AI Agents with Memory, RAG, Tool Calling, MCP, Multi-Agent Systems, and Production Workflows

Context Engineering for AI Agents: Build Reliable AI Agents with Memory, RAG, Tool Calling, MCP, Multi-Agent Systems, and Production Workflows

Benedict, Justin

Paperback

Programming

ISBN13: 9798184263144
Publisher: Independently Published
Published: Jun 25 2026
Pages: 330
Weight: 1.69
Height: 0.69 Width: 8.50 Depth: 11.00
Language: English

Stop building AI agents that forget context, hallucinate answers, and break under real-world conditions before they damage user trust and system reliability. This book gives you a practical framework for designing, deploying, and scaling reliable AI agents in production environments-not toy demos or theory-heavy experiments. Learn to build memory-enabled agents, implement Retrieval-Augmented Generation (RAG), integrate tool calling, and orchestrate multi-agent systems using proven engineering patterns and workflows. You'll discover how to create long-term memory architectures, connect agents to external tools and data sources, manage context efficiently, and implement Model Context Protocol (MCP) for scalable agent communication. You'll also learn how to evaluate performance, reduce hallucinations, improve reliability, and deploy agent systems that can operate consistently in real-world applications. Written for AI engineers, software developers, architects, and technical professionals who need production-ready AI systems rather than experimental prototypes. Drawing on practical engineering principles and modern agent-development workflows, this guide focuses on what actually works in deployment. You'll get the exact techniques needed to build AI agents that remain reliable, scalable, and effective as complexity grows.

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