7 Multi-Agent Orchestration Patterns Explained: From Parallel to Hierarchical
mdancho84 · x · 2026-10-23
Data scientist Matt Dancho notes that while 80% of data scientists want to build AI agents, almost none can answer which agentic pattern to use. His thread breaks down 7 orchestration patterns for multi-agent systems:
- Parallel: agents run simultaneously; best for independent tasks, faster output
- Sequential: agents run one after another; more reliable when steps depend on each other
- Loop: agent repeats until a condition is met; for evaluation, retry logic, quality checks
- Router: one agent decides which agent handles the task; for widely varying inputs
- Aggregator: multiple inputs feed into one agent for a single synthesized output
- Network: agents talk freely; for complex reasoning tasks
- Hierarchical: a planner agent coordinates worker agents; for scaling
The accompanying infographic also contrasts single- vs multi-agent architectures and illustrates six real-world setups including human-in-the-loop, shared tools, and shared database patterns.
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