Comparison1 min read
Single Agent vs Multi-Agent: When to Split
Multi-agent systems are fashionable. They are also more expensive, harder to debug and sometimes less accurate than one well-built agent. Start single; split for a reason.
TrueCodeAI Engineering
Agents, Voice & ML practice
- Published
Multi-agentAI agentsArchitecture
Why start with one agent
- One context, one trace — easy to debug.
- Lower token cost: no coordination overhead.
- Most business workflows fit comfortably in one agent with good tools.
Reasons to split
- Parallel work: researching many sources at once is faster with several workers.
- Context overload: one task’s material no longer fits cleanly in a single context.
- Different permissions: a read-only researcher and a write-capable executor.
- Different specialisations that genuinely need different instructions and tools.
The pattern that works
Orchestrator–worker: one agent plans and delegates; workers run focused sub-tasks in clean contexts and return concise results; the orchestrator combines them. Keep workers stateless, give each a narrow toolset, and trace every hand-off.
The costs to expect
| Single agent | Multi-agent | |
|---|---|---|
| Token cost per task | Lower | Often several times higher |
| Latency | Sequential | Can be lower with parallel workers |
| Debugging | Straightforward | Needs good tracing |
| Best for | Most workflows | Broad research, large parallel tasks |
Frequently asked questions
Is multi-agent more accurate?
On broad, parallelisable tasks it can be. On focused tasks, a single agent is often as good and cheaper.
Can we convert later?
Yes. A single agent with good tools becomes a worker in a larger system without a rewrite.
How do agents communicate?
Through structured messages via the orchestrator, not free-form chat — it keeps results predictable.
