AI Orchestration Patterns: From Single Agents to Agent Swarms

Single AI agents are powerful. But the real leverage comes from orchestration — coordinating multiple agents so they work together like a well-drilled team.

The most effective patterns we have seen in production:

**The Supervisor Pattern**: One high-level agent decomposes a task and assigns sub-tasks to specialized agents. The supervisor aggregates results and handles edge cases. Think of it as a project manager that never sleeps.

**Round-Robin Routing**: Tasks are distributed evenly across identical agents. Used for high-volume, independent tasks like content generation, data classification, or lead scoring. Simple but highly scalable.

**Hierarchical Trees**: Agents spawn child agents that spawn their own children. The root agent coordinates the entire tree. Powerful for complex research tasks that need to branch and synthesize.

**The key design principle**: Every orchestration layer needs a feedback mechanism. Agents that cannot escalate, cannot improve. Build the kill switches first, then scale up.

At Promise (streetsmartnyc.cloud/promise), we use a hybrid approach — supervisors for complex workflows, round-robin for volume. The result is systems that handle 10,000+ runs per day with sub-second latency.