Multi-agent: how to split roles to gain speed without losing control

The shift from "one agent" to "teams of agents" with coordination and specialization lets you handle complex tasks in parallel. That gives speed — if you organize roles.

Why multi-agent works

Each agent with a clear responsibility reduces conflict and duplication. You coordinate instead of implementing everything.

Recommended roles

Planner: defines steps and checkpoints. Implementer: writes code. Tester: writes tests and validates edges. Reviewer: hunts risks and inconsistencies.

Coordination protocol

Every agent writes to a shared log (what was done, what's left, questions). Mandatory checkpoint before changing architecture or data. You become execution manager: less "do everything", more "do it right and fast".

Key takeaways

Clear roles and shared log. Checkpoint before architecture changes. You coordinate, you don't implement everything.

Read also

• Refactoring with AI: how to pay down tech debt without breaking production
• Observability for AI apps: what to log so you're not flying blind

FAQ

How many agents? Start with 2–3 (e.g. planner + implementer + reviewer). Add as complexity grows.

What about cost? Multiple agents can increase model calls; set a budget and limits per task.