Multi-Agent Systems: When You Need More Than One AI

The Single-Agent Default

Start with one agent. Most businesses don't need multi-agent systems. A single well-designed agent with good tools handles 80% of use cases.

When to Go Multi-Agent

• Specialization: Different tasks need different prompts, tools, or models
• Parallel processing: Tasks run simultaneously (research + drafting)
• Failure isolation: One agent's failure shouldn't crash the workflow
• Context limits: Combined context exceeds one model's window

Architecture Patterns

Orchestrator

Orchestrator -> Research Agent / Writer Agent / Review Agent

Pipeline

Input -> Agent A -> Agent B -> Agent C -> Output

Cost Tradeoffs

Factor	Single Agent	Multi-Agent
Token cost	Lower	2-5x higher
Latency	Lower	Higher
Complexity	Simple	Significant
Debugging	Straightforward	Harder

Decision Checklist

• Can one agent handle all tools? If yes, stay single.
• Does the task need parallel work? Consider multi.
• Is your context window big enough? If no, split.
• Can you afford 2-5x token costs? If no, optimize single.