Most AI projects start with a simple prompt and response. The real challenge begins when you need that prototype to handle complex workflows, remember information, use tools, and be reliable enough for real users.
In this session, we'll explore how LangChain, LangGraph, and LangSmith work together to help you build production-ready AI agents.
Using examples from a real multi-agent educational platform built as my final-year engineering project, we'll cover how to design agent workflows, manage memory, run tasks in parallel, and debug agent behavior with confidence.
You'll learn:

What LangChain, LangGraph, and LangSmith are, and when to use each one.
How these frameworks work together to build complete AI applications.
When a simple chain is enough and when you should use a graph-based workflow.
How to give agents short-term and long-term memory.
How to build parallel agent workflows with fan-out/fan-in patterns.
How to trace, debug, and evaluate agent behavior using LangSmith.
The practical challenges of moving from a demo to a production-ready system.

By the end of the session
You'll understand how modern AI agents are built, orchestrated, monitored, and improved—and you'll have a clear roadmap for taking your own projects from prototype to production.