Generative AI has moved beyond the hype, enterprises now face the challenge of turning prototypes into scalable solutions. This book is your guide to building intelligent agents powered by LLMs. Start 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 like model selection & LLM deployment, progress to advanced methods such as Retrieval Augmented Generation, fine-tuning, in-context learning, and LLMOps especially in the context of Agentic AI. You'll 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 will learn 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 lifecycle 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)

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Spis treści

Agentic Architectural Patterns for Building Multi-Agent Systems. Proven design patterns & practices for GenAI, Agents, RAG, LLMOps & enterprise-scale AI systems eBook -- 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
• 11. Agent Frameworks: Tools for Building Agentic Applications
• 12. Use Case 1 - Single Agent (Interacting with Systems
• 13. Use Case 2 - Multi Agent System use case
• 14. Constant (Self-)Improvement: Iterative Development and Optimization
• 15. Governance and Responsible AI in GenAI & Agentic AI