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Ultimate Agentic AI with AutoGen for Enterprise Automation

Ultimate Agentic AI with AutoGen for Enterprise Automation

SKU:9789349888869

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ISBN: 9789349888951
eISBN: 9789349888869
Rights: Worldwide
Author Name: Shekhar Agrawal, Srinivasa Sunil Chippada, Rathish Mohan
Publishing Date: 30-June-2025
Dimension: 7.5*9.25 Inches
Binding: Paperback
Page Count: 356

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Description

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.

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.

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 Author & Technical Reviewer

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. Previously, he held impactful roles at IQVIA, Comcast, and AOL, delivering AI solutions that boosted operational efficiency and user experiences. With a master’s degree in electrical and computer science from the University of Cincinnati and a bachelor’s in engineering from Birla Institute of Technology, Shekhar blends deep technical expertise with strategic vision to drive innovation in data engineering and AI.

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. Previously, Rathish held senior roles at Twitch, OfferUp, and Bold, driving key initiatives such as personalization algorithms, content moderation systems, and recommender engines. He holds a master’s degree in electrical engineering from the University of Cincinnati, where his thesis focused on optimizing sensor placement for fault detection using advanced machine learning techniques. With a passion for applying AI to real-world problems, Rathish continues to push the boundaries of AI in health, e-commerce, and user personalization.

ABOUT TECHNICAL REVIEWER

Rahul Vats is a distinguished Data and AI engineering leader with over 15 years of experience architecting and delivering AI-driven solutions for Fortune 500 companies. He specializes in Generative AI, Microsoft Copilot development,Retrieval-Augmented Generation (RAG) architectures, and Agentic AI—enabling enterprises to achieve scalable, intelligent, and cost-efficient solutions. 

Currently a Senior Lead (Manager) at Capital One, Rahul leads a team of AI engineers delivering transformative solutions that integrate Generative AI with enterprise- grade data systems. His strategic initiatives have advanced Microsoft Copilot integrations, enhancing productivity through intuitive, AI-driven experiences.

Rahul's specializes in RAG architecture, building scalable knowledge retrieval pipelines for enterprise search, customer support automation, and decision-support systems. His modular frameworks have enhanced system scalability, contextual relevance, and streamlined information retrieval for enterprise users.