Lesson 1567 of 1596
AI Multi-Agent Orchestration Patterns: Supervisors, Swarms, and Pipelines
Design patterns for coordinating multiple AI agents on shared goals.
Creators · Agentic AI · ~7 min read
The premise
Multi-agent systems organize via supervisors (one delegator), swarms (peer messaging), or pipelines (fixed handoffs) — each pattern has distinct failure modes and observability needs.
What AI does well here
- Routing tasks to specialized sub-agents based on capability tags
- Aggregating sub-agent outputs into unified responses
- Detecting when a sub-agent has stalled and reassigning
- Maintaining role boundaries when given role descriptions
What AI cannot do
- Resolve genuine disagreement between two equally-confident sub-agents
- Prevent infinite delegation loops without architectural caps
Key terms in this lesson
Practice this safely
Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.
- 1Ask AI to explain supervisor pattern in plain language, then underline anything that sounds uncertain or too broad.
- 2Give it one detail from "AI Multi-Agent Orchestration Patterns: Supervisors, Swarms, and Pipelines" and ask for two possible next steps plus one reason each step might be wrong.
- 3Check agent swarm against a trusted source, teacher, adult, expert, or original document before you use it.
End-of-lesson quiz
Check what stuck
10 questions · Score saves to your progress.
Tutor
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