Single AI agents hit walls fast. They hallucinate. They lose context. They can't handle complexity.
Multi-agent orchestration solves this by breaking tasks across specialized agents that work together.
Pattern 1: Router + Specialists
One agent classifies incoming requests. Specialized agents handle each category.
Use when: You have distinct task types (support tiers, document types, query categories)
Pattern 2: Supervisor Hierarchy
A supervisor agent coordinates worker agents, validates outputs, and decides when to escalate.
Use when: Quality control matters more than speed
Pattern 3: Collaborative Swarm
Multiple agents work on the same problem from different angles. A synthesizer combines their outputs.
Use when: Complex problems benefit from diverse approaches
Pattern 4: Pipeline Orchestration
Agents in sequence, each transforming the output of the previous one.
Use when: Workflows have clear stages (extract → transform → validate → load)
Pattern 5: Human-in-the-Loop Checkpoints
Agents work autonomously until they hit decision points requiring human approval.
Use when: Errors have consequences, compliance matters, trust is building
The Meta-Pattern
All of these share one principle: narrow agents, clear handoffs, explicit coordination.
Don't build one smart agent. Build a system of focused agents that can be monitored, debugged, and improved independently.
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