Ultimate Agentic AI with AutoGen for Enterprise Automation

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 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 Automationbridges 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.

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.

Table of 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

About the Authors
Shekhar Agrawal, Senior Director of Data Science at Oracle, is an AI and data engineering expert with over 14 years of experience. He leads the development of Generative AI platforms and enterprise-scale machine learning systems that support thousands of customers worldwide.

Srinivasa Sunil Chippadais a skilled Data Science Engineering expert with 19 years of experience in building scalable enterprise data systems. He offers valuable technical insights for maximizing data value through Feature Stores, Data Marts, Data pipelines and Data Integration techniques.

Rathish Mohanis a distinguished applied scientist and AI/ML leader with over a decade of experience in machine learning, Natural Language Processing (NLP), and computer vision. He currently serves as a Senior Applied ML Scientist at Lore
Contagious Health, where he leads cross-disciplinary teams to develop advanced AI systems focused on real-time conversational AI and personalization engines, leveraging state-of-the-art technologies such as prefix tuning, LLMs, and RAG pipelines.