Most AI demos work perfectly right up until they leave the notebook.
This workshop is about closing that gap: transforming a simple prompt-and-response prototype into a system you can actually trust in production.
You'll discover why LangChain, LangGraph, and LangSmith aren't three disconnected tools, but three layers of the same challenge.
LangChain provides the building blocks: models, prompts, tools, retrieval, and integrations.
LangGraph provides orchestration: branching workflows, loops, parallel execution, state management, and failure recovery.
LangSmith provides visibility: tracing, debugging, evaluation, and observability so you can understand what your agents are really doing and improve them with confidence.
Drawing on real architectural patterns from a multi-agent educational platform that I designed, built, and defended as my final-year engineering project, including fan-out/fan-in subgraphs and a dual-memory architecture powered by Redis, we'll move beyond tutorials and explore how modern agentic systems are actually built.
In this workshop, you'll learn:

The distinct roles of LangChain, LangGraph, and LangSmith, where their responsibilities begin and end, and why understanding those boundaries matters.
How to use each framework independently and how to combine them into a cohesive production-ready stack.
How to design agent workflows as graphs rather than linear chains, and more importantly, how to recognize when graph-based orchestration is genuinely necessary.
How to implement short-term and long-term memory, along with the architectural trade-offs between persistence, context, latency, and cost.
How to build fan-out/fan-in patterns in LangGraph to execute multiple agent branches in parallel and reduce overall latency.
How to trace agent decisions, debug tool calls, and evaluate system behavior with LangSmith, replacing guesswork with observability.
How to approach testing and evaluation for agentic systems, where outputs are probabilistic rather than deterministic.
The practical challenges involved in moving from a working prototype to a deployable, observable, and maintainable production system.

By the end of this session, you'll understand not only how to build agents, but how to architect, debug, evaluate, and operate them in the real world.
Because building an AI demo is easy. Building an AI system people can rely on is where the real engineering begins.