Building Agentic AI Systems
A Hands-On Guide for Building Self-Directed, Single and Multi-Agent Solutions
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Beschreibung
This book is a comprehensive, hands-on guide designed for engineers, developers, and AI enthusiasts eager to understand and build intelligent agents capable of autonomous decision-making.
The book starts with concepts of foundation of agentic AI and explores the role of attention mechanisms and transformer architectures, key innovations that enable agents to reason contextually and perform complex tasks. Next, you will learn Large Language Models (LLMs) and Specialised Language Models (SLMs), learning how to harness them through prompt engineering, fine-tuning, and hands-on API integrations using Hugging Face and OpenAI.
From theory to practice, readers will learn to build AI agents from scratch using .NET and Python, evolving from single-agent architectures to collaborative multi-agent systems. The book covers Model Context Protocol (MCP), Agent-to-Agent (A2A) communication, enterprise guardrails, and production-ready orchestration. Readers will master agent evaluation principles, including offline benchmarking, online production evaluations, automated quality and safety assessments, performance metrics, and continuous monitoring techniques to build trustworthy, scalable, enterprise-grade AI systems.
By the end, the reader will understand the skills to not only create functional AI agents but to understand their potential, limitations, and ethical considerations. Whether you're exploring agent design or deploying at scale, this book is your roadmap to the future of AI autonomy.
What you will learn:
● Understand the core concepts, history, and distinctions of Agentic AI compared to generative and autonomous systems.
● How to build intelligent agents using LLMs, prompt engineering, and tools like LangChain and Hugging Face.
● How to build single- and multi-agent systems with memory, tool use, communication, and dynamic decision-making.
● Master AI evaluation with offline and online testing, safety and quality assessments, performance metrics, observability, and monitoring.
● Implement MCP, Agent-to-Agent (A2A) communication, enterprise guardrails, and secure tool orchestration to build production-ready AI agents.
This book is a comprehensive, hands-on guide designed for engineers, developers, and AI enthusiasts eager to understand and build intelligent agents capable of autonomous decision-making.
The book starts with concepts of foundation of agentic AI and explores the role of attention mechanisms and transformer architectures, key innovations that enable agents to reason contextually and perform complex tasks. Next, you will learn Large Language Models (LLMs) and Specialised Language Models (SLMs), learning how to harness them through prompt engineering, fine-tuning, and hands-on API integrations using Hugging Face and OpenAI.
From theory to practice, readers will learn to build AI agents from scratch using .NET and Python, evolving from single-agent architectures to collaborative multi-agent systems. The book covers Model Context Protocol (MCP), Agent-to-Agent (A2A) communication, enterprise guardrails, and production-ready orchestration. Readers will master agent evaluation principles, including offline benchmarking, online production evaluations, automated quality and safety assessments, performance metrics, and continuous monitoring techniques to build trustworthy, scalable, enterprise-grade AI systems.
By the end, the reader will understand the skills to not only create functional AI agents but to understand their potential, limitations, and ethical considerations. Whether you're exploring agent design or deploying at scale, this book is your roadmap to the future of AI autonomy.
What you will learn:
● Understand the core concepts, history, and distinctions of Agentic AI compared to generative and autonomous systems.
● How to build intelligent agents using LLMs, prompt engineering, and tools like LangChain and Hugging Face.
● How to build single- and multi-agent systems with memory, tool use, communication, and dynamic decision-making.
● Master AI evaluation with offline and online testing, safety and quality assessments, performance metrics, observability, and monitoring.
● Implement MCP, Agent-to-Agent (A2A) communication, enterprise guardrails, and secure tool orchestration to build production-ready AI agents.
Who this book is for:
This book is for AI/ML engineers wanting to move into autonomous systems, data scientists exploring advanced LLM applications and users who want to start building complex agents.
Build AI agents from scratch with LangChain, APIs, LLMs, prompt engineering, real-world architectures through examples Create conversational and multi-agent AI systems in .NET and Python using MCP, A2A, LangGraph, BabyAGI, and AutoGPT Develop production-ready AI agents with guardrails, evaluation frameworks, observability, safety metrics, and monitoring
Autor*in
Abhishek Raj Permani
Themen in »Building Agentic AI Systems«
Agentic AI Large Language Models LangChain Hugging Face LangGraph Prompt Engineering Generative AI Agents