دانلود کتاب چارچوب هوش مصنوعی عامل محور خود را بسازید -طراحی سیستم‌های چندعامله انعطاف‌پذیر در پایتون با ادغام MCP و A2A

The rapid evolution of large language models has shifted artificial intelligence from static, prompt-driven systems to dynamic, agentic architectures capable of reasoning, planning, and acting in the real world. Today’s most powerful AI applications are no longer single-model pipelines; they are systems of collaborating agents that use tools, manage context, and coordinate with one another to solve complex problems. Building such systems requires going far beyond surface-level framework usage. It demands a deep understanding of how agentic AI actually works. Rather than treating agent frameworks as black boxes, this book takes a ground-up, hands-on approach. You will explore what happens behind the curtain of modern agentic systems by building a flexible and extensible multi-agent framework in Python from first principles. Core concepts such as tool use, secure execution, context management through the Model Context Protocol (MCP), and agent collaboration via Agent-to-Agent (A2A) messaging are broken down into concrete implementations and reusable design patterns. This foundational understanding will enable you not only to build your own frameworks but also to reason about, customize, and troubleshoot agentic systems built with existing tools. The journey through the book is intentionally practical and progressive. You will begin by constructing a simple tool-using agent, then incrementally enhance its capabilities—adding secure tool execution, persistent and structured context handling, collaborative planning, and memory. As the book progresses, these individual components come together into fully autonomous, multi-agent systems capable of coordinating actions and solving complex tasks through structured communication and shared context. Complete with step-by-step walk-throughs, annotated Python code, and deep dives into realworld agent workflows, this book bridges theory and practice. By the end, you will be able to design and implement your own agentic AI systems, build custom tools for intelligent agents, integrate protocols such as MCP and A2A, and deploy collaborative AI workflows that reason, plan, and act effectively in real-world environments. Armed with these skills, you will be well prepared to build the next generation of intelligent, adaptive AI applications. The book is structured into three parts: Part 1, Foundations of Agentic AI, lays the foundation for agentic AI. It traces the evolution of generative AI, defines what an AI agent is, and distinguishes agents from chatbots. You’ll learn about core agent architecture and components, including the agent loop (sense–think–act), memory and context management, planning and reasoning, tool use, and evaluation. The part concludes with a hands-on walk-through of a simple Kubernetes-focused agent, showing how tool calling works end-to-end and how a minimal code base can deliver real autonomy. Part 2, Building Your Own Agentic AI Framework, walks you through building a real agentic AI framework (AI-6) from the ground up. You’ll explore the core engine that runs the agent loop, manages memory, sessions, tools, and multiple LLM providers, then dive into designing robust, provider-agnostic tools with schemas, validation, and execution flow. The part then shifts to user-facing interfaces, showing how to build Slack and web UIs that support visibility, control, and human-in-the-loop workflows. It closes by integrating the framework with the Model Context Protocol (MCP), enabling standardized tool discovery and interoperability across the broader ecosystem. Part 3, Constructing Multi-Agent Systems, takes you into multi-agent systems at full scale: designing agent teams, orchestration patterns, role prompts, tool scoping, and context strategies, then implementing real multi-agent workflows with the Agent-to- Agent (A2A) protocol. You’ll build a practical multi-agent DevOps system (MAKDO), learn how to test and debug agent coordination failures, and apply resilience patterns such as redundancy, graceful degradation, and human escalation. The part then moves into production deployment, walking through a realistic multi-cluster setup with secure communication and service discovery. It concludes with advanced topics and future directions, including massive context windows, long-horizon planning, multimodal systems, and the broader implications of increasingly capable agents.