Most AI demos fall apart the moment they leave a notebook. This workshop is about closing that gap, turning a prompt and response prototype into a system you'd actually trust in production.
You'll walk through how LangChain, LangGraph, and LangSmith aren't three separate tools, they're three layers of the same problem. LangChain gives you the building blocks: models, tools, retrieval. LangGraph gives you control over how those blocks think: branching, looping, fanning out into parallel subgraphs, recovering from failure. LangSmith gives you the observability to see what your agent is actually doing, trace every decision, and catch regressions before your users do.
Using real architecture patterns from a multi-agent educational platform I built and defended as my final-year engineering project, including fan-out subgraphs and dual-memory design with Redis, youwill learn :
-The core differences between LangChain, LangGraph, and LangSmith, what each one is actually for, and where their responsibilities start and stop
-How to use each framework individually, and how to wire them together so they work seamlessly as one system rather than three disconnected tools
-How to design agent workflows as graphs instead of linear chains, and just as important, how to recognize when that shift actually matters
-How to give agents short-term and long-term memory, and the architectural tradeoffs between the two
-How to build fan-out/fan-in patterns in LangGraph to run agent branches in parallel and cut down latency
-How to trace, debug, and evaluate agent and tool behavior in LangSmith, moving from guessing why an agent failed to actually seeing why
-How to think about testing agentic systems, and what "testing" even means when the output isn't deterministic
-A practical sense of what it takes to move an agent from a working prototype to something deployable and observable in production
By the end, you'll know how to take an agent from prototype to production.