 Название: Ultimate Agentic AI with AutoGen for Enterprise Automation: Design, Build, And Deploy Enterprise-Grade AI Agents Using LLMs and AutoGen To Power Intelligent, Scalable Enterprise Automation
Автор: Shekhar Agrawal, Srinivasa Sunil Chippada, Rathish Mohan
Издательство: Orange Education Pvt Ltd, AVA
Год: 2025
Страниц: 365
Язык: английский
Формат: epub (true)
Размер: 53.0 MB
Empowering Enterprises with Scalable, Intelligent AI Agents.
Key Features:
- Hands-on practical guidance with step-by-step tutorials and real-world examples.
- Build and deploy enterprise-grade LLM agents using the AutoGen framework.
- Optimize, scale, secure, and maintain AI agents in real-world business settings.
Book Description:
In an era where Artificial Intelligence (AI) is transforming enterprises, Large Language Models (LLMs) are unlocking new frontiers in automation, augmentation, and intelligent decision-making.
Ultimate Agentic AI with AutoGen for Enterprise Automation bridges the gap between foundational AI concepts and hands-on implementation, empowering professionals to build scalable and intelligent enterprise agents.
The book begins with the core principles of LLM agents and gradually moves into advanced topics such as agent architecture, tool integration, memory systems, and context awareness. Readers will learn how to design task-specific agents, apply ethical and security guardrails, and operationalize them using the powerful AutoGen framework. Each chapter includes practical examples—from customer support to internal process automation—ensuring concepts are actionable in real-world settings.
Large Language Model (LLM) agents represent a transformative advancement in Artificial Intelligence, combining sophisticated language understanding with goal-oriented behavior to perform complex tasks and assist humans across diverse domains. These AI systems leverage the capabilities of foundation models while incorporating structured decision-making frameworks to operate with increased autonomy and purpose.
The AutoGen framework is an open-source Programming Framework for Agentic AI developed by Microsoft Research to enable next-generation LLM applications with multi-agent collaborations, teachability, and personalization. The agent modularity and conversation-based programming simplify development and enable reuse for developers. It is a versatile platform designed to simplify the development and deployment of LLM agents. It provides tools and utilities for building, testing, and managing agents in enterprise environments. At the core of modern agentic AI development, AutoGen offers a comprehensive suite of capabilities that revolutionizes how artificial agents communicate, collaborate, and solve complex problems through its thoughtfully designed architecture and powerful integration options.
By the end of this book, you will have a comprehensive understanding of how to design, develop, deploy, and maintain LLM-powered agents tailored for enterprise needs. Whether you're a developer, data scientist, or enterprise architect, this guide offers a structured path to transform intelligent agent concepts into production-ready solutions.
What you will learn:
- Design and implement intelligent LLM agents using the AutoGen framework.
- Integrate external tools and APIs to enhance agent functionality.
- Fine-tune agent behavior for enterprise-specific use cases and goals.
- Deploy secure, scalable AI agents in real-world production environments.
- Monitor, evaluate, and maintain agents with robust operational strategies.
- Automate complex business workflows using enterprise-grade AI solutions.
Who is this book for?
This book is tailored for AI/ML engineers, software developers, data scientists, solution architects, enterprise tech leads, product managers, innovation strategists, and CTOs. It’s also valuable for business leaders and decision-makers seeking to understand and leverage LLM-powered agentic systems for scalable, intelligent enterprise solutions.
Contents:
1. Introduction to LLM Agents (Foundation and Impact)
2. Architecting LLM Agents (Patterns and Frameworks)
3. Building a Task-Oriented Agent Using AutoGen
4. Integrating Tools for Enhanced Functionality
5. Context Awareness and Memory System
6. Designing Multi-Agent Systems
7. Evaluation Framework for Agents and Tools
8. Agent-Security, Guardrails, Trust, and Privacy
9. LLM Agents in Production
10. Use Cases for Enterprise LLM Agents
11. Advanced Prompt Engineering for Effective Agents
Index
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