Advanced RAG Course: Build Text to SQL Agentic AI System
Build production agentic AI systems, from architecture to deployment.
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Multi-agent systems earn their complexity when one agent with one prompt is no longer enough. Research plus write, plan plus execute, triage plus resolve. You end up wanting different roles with different models, clean handoffs between them, and a supervisor that decides who runs next.
Curated by Param Harrison
These courses teach the patterns that actually hold up in production: supervisor graphs in LangGraph, group chat in AutoGen, document analyzers that run parallel agents, and voice systems where a frontline agent hands off to a specialist. You learn where multi-agent pays off and, just as importantly, where one well-designed agent is still the right answer.
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Common questions
When the work decomposes naturally (research, then write; plan, then execute) or when each sub-task needs a different model, toolset, or system prompt. If your problem is really a prompt problem, more agents will make it worse.
Supervisor for most cases. A central node decides who goes next, which gives you one place to reason about routing, retries, and cost. Peer-to-peer group chat is useful for brainstorming patterns but harder to debug in production.
LangGraph when you want explicit graphs, checkpointing, and strong observability hooks. AutoGen when conversation-between-agents is the core pattern and you want the boilerplate handled. Both are covered here because teams ship both.
Pass summaries, not transcripts. Each agent compresses its output to the minimum the next agent needs, and the supervisor keeps a short running log. The multi-agent voice systems course shows this pattern applied to real-time handoffs.
Trace every node call, every tool result, and every handoff into a single timeline. The observability course covers the tracing setup. Without it, multi-agent debugging is guessing which agent got confused first.