Ultimate Agentic AI with AutoGen for Enterprise Automation

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 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. 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. 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 Chippada is 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. Passionate about scaling Data Capabilities he provides strategic technical insights to help organizations implement their data-driven visions. Rathish Mohan is 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.