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Agentic AI Systems for Developers: A Developer's Guide for Designing, Debugging, and Scaling Production-Ready Multi-Agent Systems
Paperback
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ISBN13: 9798267184533
Publisher: Independently Published
Published: Sep 26 2025
Pages: 264
Weight: 1.02
Height: 0.55 Width: 7.00 Depth: 10.00
Language: English
Publisher: Independently Published
Published: Sep 26 2025
Pages: 264
Weight: 1.02
Height: 0.55 Width: 7.00 Depth: 10.00
Language: English
Agentic AI Systems for Developers
A Developer's Guide for Designing, Debugging, and Scaling Production-Ready Multi-Agent Systems
Intelligent agents are no longer a research experiment, they are the foundation of modern AI applications. But building production-ready agentic systems requires more than wiring a large language model to a few APIs. Developers need architectures, orchestration strategies, and debugging methods that scale.
A Developer's Guide for Designing, Debugging, and Scaling Production-Ready Multi-Agent Systems
Intelligent agents are no longer a research experiment, they are the foundation of modern AI applications. But building production-ready agentic systems requires more than wiring a large language model to a few APIs. Developers need architectures, orchestration strategies, and debugging methods that scale.
This book shows you how to design and deploy multi-agent systems that communicate, collaborate, and complete real-world workflows. You will learn how to move beyond toy demos into robust, enterprise-ready pipelines using frameworks like Claude Subagents, LangGraph, LangChain, and AutoGen.
What You Will Learn
Design agent lifecycles with planning, execution, memory, and verification stages
Implement orchestration patterns including single-agent pipelines, multi-agent collaboration, and graph-based workflows
Debug and monitor agent communication, state transitions, and error cascades
Integrate with real tools and data through APIs, embeddings, and external knowledge bases
Secure and govern systems with role-based access, tool whitelisting, and human-in-the-loop checkpoints
Scale to production with fault tolerance, checkpointing, retries, and cost-optimized deployments
Who This Book Is For
Developers building intelligent assistants or domain-specific AI tools
AI engineers designing agentic workflows for production systems
Data scientists extending LLMs with orchestration, retrieval, and automation
Researchers exploring communication, negotiation, and emergent behavior in agent teams
Inside the Book
Real-world case studies: customer support automation, SRE workflows, and research assistants
Fully runnable Python implementations with LangGraph and LangChain
Best practices checklists and common pitfalls with mitigation strategies
Guidance on testing, observability, and compliance for enterprise contexts
If you are ready to move beyond prompt engineering and build agentic AI systems that work together as teammates, this book will show you the way.
What You Will Learn
Design agent lifecycles with planning, execution, memory, and verification stages
Implement orchestration patterns including single-agent pipelines, multi-agent collaboration, and graph-based workflows
Debug and monitor agent communication, state transitions, and error cascades
Integrate with real tools and data through APIs, embeddings, and external knowledge bases
Secure and govern systems with role-based access, tool whitelisting, and human-in-the-loop checkpoints
Scale to production with fault tolerance, checkpointing, retries, and cost-optimized deployments
Who This Book Is For
Developers building intelligent assistants or domain-specific AI tools
AI engineers designing agentic workflows for production systems
Data scientists extending LLMs with orchestration, retrieval, and automation
Researchers exploring communication, negotiation, and emergent behavior in agent teams
Inside the Book
Real-world case studies: customer support automation, SRE workflows, and research assistants
Fully runnable Python implementations with LangGraph and LangChain
Best practices checklists and common pitfalls with mitigation strategies
Guidance on testing, observability, and compliance for enterprise contexts
If you are ready to move beyond prompt engineering and build agentic AI systems that work together as teammates, this book will show you the way.
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