http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Agentic architectural patterns for building multi-agent systems
proven design patterns & practices for GenAI, Agents, RAG, LLMOps & enterprise-scale AI systems
Transform GenAI experiments into production-ready intelligent agents with scalable AI systems, architectural patterns, frameworks, and responsible AI and governance best practicesFree with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Build robust single and multi-agent GenAI systems for enterprise use...
Intro -- Agentic Architectural Patterns for Building Multi-Agent Systems -- Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems -- Foreword -- Contributors -- About the authors -- About the reviewers -- Join our Discord and Reddit space -- Table of Contents -- Preface -- Who this book is for -- What this book covers -- To get the most out of this book -- Download the example code files -- Download the color images -- Conventions used -- Get in touch -- Free Benefits with Your Book -- How to Unlock -- Share your thoughts -- Part 1
Foundations and Core Agent Concepts -- 1 -- GenAI in the Enterprise: Landscape, Maturity, and Agent Focus -- The transformative potential of GenAI -- Overview of business applications -- Horizontal applications (cross-functional use cases) -- Vertical or domain-specific applications -- Introducing agentic AI systems -- The anatomy of agentic AI -- Core components -- Agent anatomy -- Data stores and environment context -- Key architectural features -- The GenAI Maturity Model: a path to agentic systems -- The new agentic stack -- Enabling agent communication: from tools to collaboration
Agent internals (common to A and B for simplicity) -- The MCP server -- The agent server -- Challenges hindering production-grade GenAI -- Summary -- Get This Book's PDF Version and Exclusive Extras -- 2 -- Agent-Ready LLMs: Selection, Deployment, and Adaptation -- Role of LLMs in agentic systems -- Model selection: choosing the right foundation -- Context window size -- Model size and specialization for agents -- Native support for tool use and function calling -- Model robustness, reliability, and safety -- Adaptability and fine-tuning potential -- Other key selection considerations
Deployment and performance optimization for agents -- Serving architectures for agentic LLMs -- Cloud-hosted APIs -- Self-hosted models -- Edge deployment -- Performance optimization strategies -- Latency reduction -- Throughput maximization -- Cost optimization -- Optimizing for tool interaction -- Security considerations in LLM deployment for agents -- AgentOps: managing LLMs in agentic systems -- Summary -- Subscribe for a free eBook -- 3 -- The Spectrum of LLM Adaptation for Agents: RAG to Fine-tuning -- From generic LLMs to specialized agents -- Another maturity model for agentic AI
The granularity of agents -- A hierarchical agentic architecture for business process automation -- The core components: agents and their capabilities -- The hierarchical structure: orchestrators and specialists -- Governance and observability via callbacks -- Contextual enhancement: stage 1, enhancement with RAG -- RAG-powered customer support agent -- Financial analyst agent leveraging RAG for timely market insights -- Compliance agent ensuring adherence with RAG in transaction monitoring -- Analyzing the scenarios -- Fine-tuning for agentic capabilities -- Domain specialization
Birmingham : Packt Publishing, Limited,c2026