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Designing Agentic AI Systems: Patterns Protocols and Frameworks for LangGraph MCP and Aut

by Todd Chandler
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Current price ₹1,684.00
Original price ₹2,219.00
Original price ₹2,219.00
Original price ₹2,219.00
(-24%)
₹1,684.00
Current price ₹1,684.00

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Book cover type: Paperback
  • ISBN13: 9798286838233
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 304
  • Original Price: GBP 17.0
  • Language: English
  • Edition: N/A
  • Item Weight: 409 grams
  • BISAC Subject(s): Artificial Intelligence / General

Facing the challenge of orchestrating AI agents that think, act, and adapt across multiple services? "Designing Agentic AI Systems" delivers a complete blueprint for building autonomous workflows with real-world reliability.

At its core, this book reveals how to transform standalone language models into coordinated, goal-driven agents that call external APIs, manage state, and recover from errors. You'll discover proven design patterns (Chapter 2), a standardized Model Context Protocol (MCP) for seamless agent-tool communication (Chapter 3), and hands-on tutorials for LangGraph and AutoGen-two frameworks that turn ideas into production-ready pipelines (Chapters 4-6).

What You'll Master:

  • Agent-Driven Workflows: Learn task decomposition, dynamic replanning, and error-handling strategies that keep your agents running smoothly, even under failure conditions.

  • Interoperable Protocols: See how MCP message schemas enable transparent, auditable exchanges between agents and external services, ensuring every API call is logged, validated, and retried if necessary.

  • Graph-Based Orchestration with LangGraph: Build, test, and deploy stateful workflows (Chapter 4). Define nodes and edges to link LLM reasoning, tool invocation, and memory layers into a cohesive pipeline.

  • Multi-Agent Coordination via AutoGen: Define roles, create task-handoff scenarios, and share memory across agents (Chapter 5). Implement KV and vector memory layers for persistent context.

  • Performance and Scalability: Explore horizontal versus vertical scaling strategies, GPU batching, and caching techniques to optimize resource usage (Chapter 11).

  • Security, Compliance, and Ethics: Protect agent-tool communications with authentication and role-based authorization, safeguard sensitive data, and implement bias-detection checks (Chapter 10).

Whether you're an AI engineer, developer, or technical lead, this book equips you with the practical skills and code examples needed to architect agentic AI systems that are robust, scalable, and maintainable.

Take control of your AI workflows today, order "Designing Agentic AI Systems" and build autonomous agents your users can trust.

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