Curriculum

14 Chapters · 25 Lessons

    1. Ultimate Agentic AI with AutoGen for Enterprise Automation Free preview

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      Introduction

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      Introduction to LLM Agents (Foundation and Impact)

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      Introduction to LLM Agents (Foundation and Impact)

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      Architecting LLM Agents (Patterns and Frameworks)

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      Architecting LLM Agents (Patterns and Frameworks)

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      Building a Task-Oriented Agent Using AutoGen

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      Building a Task-Oriented Agent Using AutoGen

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      Integrating Tools for Enhanced Functionality

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      Integrating Tools for Enhanced Functionality

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      Context Awareness and Memory System

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      Context Awareness and Memory System

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      Designing Multi-Agent Systems

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      Designing Multi-Agent Systems

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      Evaluation Framework for Agents and Tools

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      Evaluation Framework for Agents and Tools

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      Agent-Security, Guardrails, Trust, and Privacy

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      Agent-Security, Guardrails, Trust, and Privacy

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      LLM Agents in Production

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      LLM Agents in Production

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      Use Cases for Enterprise LLM Agents

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      Use Cases for Enterprise LLM Agents

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      Advanced Prompt Engineering for Effective Agents

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      Advanced Prompt Engineering for Effective Agents

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      INDEX

About the course

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. Start building the next generation of enterprise AI agents with AutoGen—today.

About the Author

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. Known for his technical leadership, he has built robust AI governance frameworks, integrating innovative technologies such as Kubernetes, Spark, and Hadoop.  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. He has a Double Masters (MIS, MBA), is a certified Project Management Professional, and holds several technical Certifications. 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.