The Rise of Generative AI: From Language Models to Agents
The gap between experimental and production-ready agents is stark. According to LangChain’s State of Agents report, performance quality is the #1 concern among 51% of companies using agents, yet only 39.8% have implemented proper evaluation systems. Our book bridges this gap on two fronts: first, by demonstrating how LangChain and LangSmith provide robust testing and observability solutions; second, by showing how LangGraph’s state management enables complex, reliable multi-agent systems. You’ll find production-tested code patterns that leverage each tool’s strengths for enterprise-scale implementation and extend basic RAG into robust knowledge systems.
LangChain accelerates time-to-market with readily available building blocks, unified vendor APIs, and detailed tutorials. Furthermore, LangChain and LangSmith debugging and tracing functionalities simplify the analysis of complex agent behavior. Finally, LangGraph has excelled in executing its philosophy behind agentic AI – it allows a developer to give a large language model (LLM) partial control flow over the workflow (and to manage the level of how much control an LLM should have), while still making agentic workflows reliable and well-performant.
In this chapter, we’ll explore how LLMs have evolved into the foundation for agentic AI systems and how frameworks like LangChain and LangGraph transform these models into production-ready applications. We’ll also examine the modern LLM landscape, understand the limitations of raw LLMs, and introduce the core concepts of agentic applications that form the basis for the hands-on development we’ll tackle throughout this book.
In a nutshell, the following topics will be covered in this book:
- The modern LLM landscape
- From models to agentic applications
- Introducing LangChain
