Clawd Control: Real‑Time Ops for Clawdbot Agent Fleets

🤔 Curiosity: The Question

Once you run more than one agent, the problem flips from “can it work?” to “can I trust and operate it?” I kept asking: Where is my mission control for agent fleets?

Clawd Control claims to be that layer: a real‑time dashboard for Clawdbot agents. I wanted to know if it’s just a UI—or a real ops harness.

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📚 Retrieve: The Knowledge

What Clawd Control Is

From the repo and the walkthrough, Clawd Control is a lightweight, real‑time monitoring dashboard for Clawdbot agents. It shows fleet health, per‑agent detail, and host resource usage—all in a single screen.

Key capabilities:

• Live monitoring via Server‑Sent Events (SSE)
• Fleet overview with health indicators
• Agent detail view (sessions, channels, config, env)
• Agent creation wizard for fast onboarding
• Host metrics (CPU/RAM/Disk) for infra‑vs‑agent debugging
• Auto‑discovery of local agents

Architecture: Intentionally Simple

Clawd Control avoids heavy frameworks:

• Single Node.js server
• No build step
• Vanilla HTML/JS frontend
• A small set of modules: server.mjs, collector.mjs, discover.mjs, create-agent.mjs

This keeps deployment fast and debuggable.

Quick Start (from repo)

git clone https://github.com/Temaki-AI/clawd-control.git
cd clawd-control
npm install
npm start

Then open http://localhost:3100 and log in with the generated password.

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💡 Innovation: The Insight

Why This Matters in Practice

Agent systems fail silently if you can’t see them. Clawd Control turns opaque agents into observable services. In real ops, that means:

• faster detection when an agent hangs
• clear separation between agent bugs vs resource bottlenecks
• safer scaling when the fleet grows

A Minimal Ops Checklist

1) Secure access

• use the generated password; rotate via auth.json 2) Set polling intervals
• tune pollIntervalMs and hostMetricsIntervalMs 3) Register remote agents
• add them to agents.json with host/port/token 4) Observe before you automate
• dashboards first, auto‑actions later

Why This Matters for AI × Games

Live‑ops and QA pipelines need predictable agent behavior. A monitoring layer like this is the bridge between “agent demos” and agent production.

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New Questions This Raises

• What’s the right alerting layer on top of this dashboard?
• How do we standardize metrics across different agent types?
• When should the dashboard trigger automated mitigation?

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References

1) Clawd Control repo:
https://github.com/Temaki-AI/clawd-control

2) Clawd Control ops guide (Korean):
https://digitalbourgeois.tistory.com/m/2768