We've taught LangChain and LangGraph multi-agent workshops at Grace Hopper two years running to 200+ developers each time. The number one question we get after every session: "This works in a demo — but how do I get past the walls when I try to scale it?"
We know those walls firsthand. We've built agentic AI systems across POCs and production in global banking — and between us, we've hit every failure mode: agents losing context mid-task, orchestration logic that doesn't survive real workloads, and delegation patterns that look clean on a whiteboard but collapse under pressure.
DeepAgents is a new LangChain framework built to solve exactly these problems — intelligent delegation, advanced planning, robust context preservation, and error recovery designed for production-grade workflows. In this 120-minute hands-on workshop, we'll build a real multi-agent system together in GitHub Codespaces — no setup, no installs, just code.
Through guided labs, you'll architect a coordinator that delegates research to one agent, analysis to another, and synthesis to a third. You'll implement human-in-the-loop approval gates, long-term memory, and the error recovery patterns that separate prototypes from systems that survive production. We'll be honest about what's battle-tested and what's cutting-edge — because that's the conversation practitioners actually need.
What you'll walk away with: a portfolio-ready multi-agent system you built yourself, production skills across backends, subagents, coordinators, memory, and debugging, a complete code repository with reusable templates and documentation you can extend immediately, and the framework decision-making to know when LangChain chains, LangGraph, or DeepAgents is the right tool for your problem.
Bring a GitHub account and a browser. Leave with production-tested patterns.