AI agents are redefining how modern businesses work—shifting from simple chatbots to digital workers capable of reasoning, planning, using tools, and coordinating in multi step workflows. This book is a hands on guide to building these systems using the two most powerful open source frameworks ie LangChain and AutoGen. Written for developers, analysts, and technical managers, the book shows how to turn large language models into practical, reliable agents that deliver measurable business value. 

Across nine structured chapters, the book moves from foundations to production grade agent deployments. Part I introduces agent theory, decision frameworks, and core tools through a rapid, code first curriculum. You learn LangChain essentials—chains, prompts, RAG, routing, fallback logic—and AutoGen fundamentals such as multi agent chats, role assignments, tool execution, and error handling. Part II dives into building real systems: ReAct agents, custom tool suites, retrieval augmented agents, multi agent AutoGen teams, approval workflows, and sequential pipelines for research, writing, and analysis. Every chapter alternates between short explanations and extensive hands on coding, with ready to run Jupyter notebooks that work in Colab or Binder. You follow complete implementations—from “Hello Agent” to full multi agent solutions—using the exact standards companies expect today, including LCEL, LangSmith, and modern API patterns. 

Designed for real work rather than theory, this book gives you the skills to build, scale, and deploy AI agents that solve customer support, research, supply chain, and automation challenges. 

What you will learn:

Build practical AI agents with LangChain and AutoGen, from simple chains to multi agent teams for real business tasks.
• Deploy production ready agents with error handling, LangSmith monitoring, FastAPI endpoints, Docker, and runnable notebooks.
• Master core patterns such as LCEL chains, ReAct reasoning, tool use, group chats, human in loop steps, RAG pipelines, and cost efficient scaling.
• Apply agents to real business cases—including supply chain optimization, automated research, and ticket classification—with portfolio ready projects.
 
Who this book is for:

The book is written for developers, data scientists, and AI/ML practitioners who want to build practical agentic AI systems using LangChain and AutoGen. It also serves product managers, solution architects, and engineering leads responsible for bringing agent workflows into real business environments. Business analysts and operations professionals looking to automate customer support, research, and supply chain tasks will also benefit.

Shailendra Kadre is known for technical expertise, education, and authorship, particularly in fields such as Artificial Intelligence, Data Science, and Business Analytics. His work often bridges the gap between technical concepts and their practical, business-oriented applications

Shailendra Kadre is currently associated with Christ University’s School of Business in Bangalore, India, specialising in AI, product analytics, machine learning, deep learning, and natural language processing. He has authored several books, including “Going Corporate: A Geek’s Guide”, Practical Business Analytics Using SAS, Machine Learning and Deep Learning Using Python and TensorFlow, and Mastering Text Analytics. All his books are published in the USA by Springer and McGraw-Hill. He is also an active YouTuber with the channel THE AI CLUB by Shailendra Kadre, where he shares tutorials on AI topics.

Shailendra Kadre holds a master’s degree in design from the Indian Institute of Technology (IIT) Delhi, and his second master’s degree is in AI from a public UK university. Currently, he resides in Bangalore, India, with his wife, Meenakshi, daughter, Neha, and Son, Vivek.

Dr. Ankit A. Jain is an AI and Data Science practitioner with over 15 years of experience in machine learning, analytics, MLOps, recommendation systems, and Generative AI applications across retail, petrochemical, oil and gas, supply chain, and intelligent automation domains. He holds a PhD from IIT Bombay and has worked on a wide range of applied AI and data-driven problem-solving initiatives.

His interests include Retrieval-Augmented Generation (RAG), AI Agents, LangChain workflows, and scalable AI system design. Through this book, he aims to explain modern AI engineering concepts using practical examples and hands-on implementations that help readers bridge the gap between theory and real-world applications.

Nishant Kumar is an Associate Professor of Marketing at CHRIST (Deemed to be University), Bengaluru, India. He is a faculty member in business and management with 16 years of rich experience in industry and academia. His contributions to scholarly literature span over 40 publications in SCOPUS, SSCI, and ABDC-indexed journals.  His work frequently leverages tools such as Python, PLS-SEM, SPSS, AMOS, Tableau, Power BI, and generative AI to derive actionable insights and support evidence-based decision-making. He successfully launched two SWAYAM courses in Gemification and Artificial Intelligence in collaboration with IIMBX. His interests and expertise include artificial intelligence, business analytics, Gen AI, intelligent computation technologies, blockchain, and its application in business.

He is currently a Professor at Christ University’s School of Business, specialising in all areas of Artificial Intelligence, GenAI, Analytics, Machine Learning, Blockchain, Digital Marketing and Quantum Technologies. 

Krishna Durbha has over 32 years of industry experience, including at CxO levels, across various sectors. He has led large teams and has managed significant P&L responsibilities. He is consulted for many Global Fortune 500 companies on large technology, analytics and AI engagements.  

A few of the clients include Microsoft, Telenor, Telstra, AT&T, PCCW, Time, NYT, NBC Universal, Disney, WPP group companies, Huawei and many others. He has set up and led large revenue P&L portfolios exceeding US$2 billion in the telecom sector.