Agentic Architectural Patterns for Building Multi-Agent Systems. Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-s Strumień

Generative AI has moved beyond the hype, and enterprises now face the challenge of turning prototypes into scalable solutions. This book is your guide to building intelligent agents powered by LLMs.Starting with a GenAI maturity model, you’ll learn how to assess your organization’s readiness and create a roadmap toward agentic AI adoption. You’ll master foundational topics such as model selection and LLM deployment, progressing to advanced methods such as RAG, fine-tuning, in-context learning, and LLMOps, especially in the context of agentic AI. Youll explore a rich library of agentic AI design patterns to address coordination, explainability, fault tolerance, and human-agent interaction. This book introduces a concrete, hierarchical multi-agent architecture where high-level orchestrator agents manage complex business workflows by delegating entire sub-processes to specialized agents. You’ll see how these agents collaborate and communicate using the Agent-to-Agent (A2A) protocol.To ensure your systems are production-ready, we provide a practical framework for observability using life cycle callbacks, giving you the granular traceability needed for debugging, compliance, and cost management. Each pattern is backed by real-world scenarios and code examples using the open source Agent Development Kit (ADK).*Email sign-up and proof of purchase required Spis treści: 1. GenAI in the Enterprise: Landscape, Maturity, and Agent Focus 2. Agent-Ready LLMs: Selection, Deployment, and Adaptation 3. The Spectrum of LLM Adaptation for Agents: RAG to Fine-tuning 4. Agentic AI Architecture: Components and Interactions 5. Multi-Agent Coordination Patterns 6. Explainability and Compliance Agentic Patterns 7. Robustness and Fault Tolerance Patterns 8. Human-Agent Interaction Patterns 9. Agent-Level Patterns 10. System-Level Patterns for Production Readiness 11. Advanced Adaptation: Building Agents That Learn 12. A Practical Roadmap: Implementing Agentic Patterns by Maturity Level 13. Use Case: A Single Agent for Loan Processing 14. Use Case: A Multi-Agent System for Loan Processing 15. Agent Frameworks: – Use Case: A Multi-Agent System for Loan Processing with CrewAI and LangGraph 16. Conclusion: Charting Your Agentic AI Journey