Module 1: The Mental Model•Lesson 3 of 5
Coordinator vs Worker Agents
Coordinator vs Worker Agents
Not all agents are created equal. There are two fundamentally different types:
Coordinator Agents (The Thinkers)
Job: Understand intent, plan work, delegate, synthesize results
Characteristics:
- Always running (your main session)
- Has full context (memory, user info, project state)
- Uses a powerful model (Claude Opus, GPT-4)
- Spawns sub-agents when needed
- Maintains continuity across conversations
Example:
User: "Research the top 5 competitors to Skillbase"
Coordinator thinks:
- This needs web research
- Should spawn a researcher agent
- I'll synthesize results when it's done
Coordinator acts:
- Spawns researcher with task
- Continues other work
- Receives results
- Summarizes for userWorker Agents (The Doers)
Job: Execute specific tasks, report results
Characteristics:
- Spawned on demand
- Has limited context (just the task)
- Can use a cheaper/faster model (Claude Sonnet, GPT-4-mini)
- Dies when task is complete
- No persistent memory
Example:
Researcher agent receives:
"Find top 5 competitors to Skillbase (soft skills training app)"
Researcher does:
- Searches web for "soft skills training apps"
- Reads competitor websites
- Compiles comparison
- Returns structured results
Researcher terminates after reporting back.When to Use Each
| Situation | Use |
|---|---|
| Ongoing conversation | Coordinator |
| Background research | Worker |
| Quick question | Coordinator |
| Long-running task | Worker |
| Needs memory/context | Coordinator |
| Isolated computation | Worker |
| Might spawn other tasks | Coordinator |
| Simple input → output | Worker |
The Pattern
COORDINATOR (Opus, full context, persistent)
- Spawns workers as needed
- Stays responsive
⬇️ spawn ⬇️
WORKER 1 (Sonnet) → Research task WORKER 2 (Sonnet) → Execute task
⬇️ results ⬇️
Back to COORDINATOR for synthesis
This pattern is powerful because:
- Coordinator stays responsive (not blocked by long tasks)
- Workers can run in parallel
- You can use cheaper models for simple tasks
- Each component does one thing well
Real Example From My Setup
When I say "create the OpenClaw course landing page":
- Coordinator (me, Alex) understands the request
- Coordinator decides what needs to happen
- Coordinator executes directly (this was simple enough)
But when I need to extract facts from conversations:
- Coordinator recognizes the pattern
- Cron job spawns a Sonnet worker every 2 hours
- Worker reads recent conversation
- Worker extracts durable facts
- Worker writes to memory files
- Worker terminates
The coordinator stays light. Workers do the heavy lifting.