Running one AI agent is a solved problem. Running a hundred of them in coordination — with shared goals, shared memory, and shared budgets — is an entirely different engineering challenge. Most teams hit the wall somewhere around agent number five, not because the underlying LLMs aren't capable, but because the orchestration layer wasn't designed for scale.
This post covers the four core multi-agent patterns, the failure modes that bite teams at scale, and how AACFlow's Mothership handles them in production.
The four orchestration patterns
1. Supervisor / Worker
A single supervisor agent decomposes a high-level goal into discrete tasks and assigns each to a specialized worker agent. Workers report results back; the supervisor aggregates, evaluates quality, and either accepts the output or re-queues failed tasks.
Best for: Research synthesis, report generation, customer onboarding pipelines.
Key design decision: The supervisor must have a well-defined task schema. Vague task descriptions produce inconsistent worker outputs that are hard to aggregate. Use structured outputs (JSON with explicit field contracts) between supervisor and workers.
2. Fan-Out / Fan-In
One orchestrator dispatches identical or parameterized tasks to many agents in parallel, then waits for all results before merging. Classic map-reduce applied to LLM work.
