Why more than one agent?

When you start, a single agent is enough. But as you give it more responsibilities, you reach a point where a single agent isn’t the best solution.

Reasons for separating agents

Model specialisation: Different tasks require different models. A daily briefing doesn’t need the most powerful model. A complex analysis does.

Cost: Separating by model lets you assign the expensive model only to tasks that need it.

Availability: If one agent fails, the others keep running.

Organisation: Each agent with its own system prompt, skills, and memory.

Practical example: two coordinated agents

Primary agent (quality): Claude Sonnet or GPT-4. For interactive conversation, important decisions.

Secondary agent (volume): Kimi K2.5 via NVIDIA NIM or LLaMA via Groq. For automatic tasks, briefings, translations. Near-zero cost.

Workspaces in OpenClaw

OpenClaw supports workspaces — separate environments with independent configuration. Each workspace can have a different model, different skills, and a different system prompt.

Key concepts today

  • Separate by cost: Expensive model for quality, cheap model for volume
  • Separate by function: Each agent an expert in its domain
  • Workspaces: Separate environments within OpenClaw
  • Don’t overcomplicate: If one agent is enough, one is better than two

Next lesson: Security and privacy — your agent in the real world.