Multi-Agent Engineering: Build Autonomous Systems with LangChain & LangGraph

Understand the 5 core multi-agent patterns: Subagents, Handoffs, Skills, Router and Custom Workflows

Learn when each pattern shines and what tradeoffs it introduces in real enterprise systems

Study visual architecture diagrams that map each pattern to its real-world structure and data flow

Apply a practical decision rubric to evaluate any task against the 5 core patterns

Avoid the costly mistake of over-engineering — know when a single agent is actually enough

Walk away with a reusable framework you can apply to every multi-agent decision at work

Use LangGraph's persistence layer to maintain state across long-running, multi-step workflows

Configure per-invocation, per-thread, and stateless checkpointing for the right use case

Build pipelines that resume gracefully from failures without losing progress or corrupting state

Wire multiple specialized agents together using the right patterns and communication systems

Implement agent-to-agent handoffs that pass context cleanly across role boundaries

Build systems where agents delegate, escalate, and collaborate just like a real human team would

Build a fully working AI-powered Job Interview Pipeline from architecture through to deployment

Apply every pattern taught in the course inside one cohesive, enterprise-grade system

Leave with a portfolio project that demonstrates real multi-agent engineering to any employer

Design human-in-the-loop touchpoints that give users the right level of control and transparency

Learn how to surface agent progress, decisions, and errors in ways that build user trust

Build agent interactions that feel seamless — not like a black box dumping results on the user