AI Agents in Business
Mastering LangChain and AutoGen
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Beschreibung
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.
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.
Build three GitHub ready projects: a customer agent, a supply chain dashboard, and an automated research pipeline Focus on real business use cases with production grade customer support, supply chain, and research agent workflows Explores both LangChain LCEL and AutoGen multi-agent systems with LangSmith monitoring and FastAPI deployment
Autor*in
Shailendra Kadre
Themen in »AI Agents in Business«
AutoGen multi-agent systems AI agents Python code ReAct agent implementation Multi-agent workflows RAG agents LangChain Production AI agents Agentic AI frameworks LangChain agents tutorial