28 · Single vs Multi-Agent Systems

This helps you choose the simplest architecture that solves the problem: start with one agent and add more only when you need to. It compares single-agent and multi-agent systems and their trade-offs.

How it works

1. 1Single agent (do-it-all): one agent plans, uses tools and gets the job done.
2. 2Multi-agent (specialists): a supervisor/orchestrator coordinates research, analysis, execution and review agents.
3. 3Multi-agent patterns: supervisor (centralized), hierarchical, peer-to-peer, pipeline, parallel, reflection/critic.
4. 4Design guidelines: start single, add specialization, give clear roles and structured output.

Key points

• Use single agent for simple/moderate tasks, few tools, low coordination.
• Use multi-agent for complex, multi-domain, long-running or high-accuracy work.
• Trade-offs: multi-agent adds latency, cost and complexity but scales specialization.
• Design for impact, not for a demo.

Why it matters

Over-engineering with many agents adds cost and fragility. Choosing single vs multi-agent deliberately keeps systems simple, debuggable and reliable.

Frequently asked questions

When should I stay single-agent?

For straightforward tasks with few tools and low coordination — it's simpler and cheaper to debug.

What do multi-agent systems cost?

More latency, tokens and complexity, in exchange for specialization and scale on complex goals.