Mission Control: Building a 10-Agent AI Squad with OpenClaw
"10 agents. One lead. Running 24/7. They create tasks, claim them, execute, and review each other's work."
Bhanu Teja P's viral X post on building Mission Control has nearly 3 million views and hundreds of replies. It's a fascinating proof-of-concept showing what's possible with multi-agent OpenClaw systems.
It's also a cautionary tale about what happens when you run powerful agents without guardrails.
Let's break down how Mission Control works, what problems emerged, and how to deploy similar systems safely.
What is Mission Control?
Mission Control is a multi-agent system built on OpenClaw—an open-source AI agent framework. The setup consists of:
- 10 autonomous agents, each with specialized roles
- One lead agent ("Jarvis") that coordinates the squad
- 24/7 operation without human intervention
- Inter-agent communication via threads and group chats
- Self-organizing task management where agents create, claim, and execute work
Think of it as a digital team where the AI agents work like employees: one manages, others specialize, and they collaborate to produce output.
The Agent Squad Structure
| Role | Function |
|---|---|
| Jarvis (Lead) | Coordinates tasks, assigns work, resolves conflicts |
| Research Agent | Gathers information, validates facts |
| Writer Agent | Creates content, drafts documents |
| Reviewer Agent | Quality checks other agents' work |
| Code Agent | Implements features, fixes bugs |
| Analyst Agent | Processes data, generates insights |
| + 4 more specialists | Domain-specific tasks |
Key Behaviors
The agents don't just execute commands—they collaborate:
- Task creation: Agents identify and create their own tasks
- Claiming work: Agents claim tasks they're suited for
- Discussion: Agents debate approaches in threads
- Review loops: Agents refute, praise, and improve each other's work
- Escalation: Contentious issues get escalated to Jarvis
Each agent has its own "SOUL" (a configuration file defining personality, skills, and prompts). This is what makes each agent specialized rather than generic.
Why It Went Viral
The appeal is obvious:
- Autonomous productivity — Work happens while you sleep
- Scalable expertise — 10 specialists instead of one generalist
- Human-like collaboration — Agents debate and improve output
- Open source — Anyone can replicate it with OpenClaw
Bhanu tied the setup to his SiteGPT journey from $10K to $1M ARR, framing it as a serious business tool rather than a toy.
The post inspired hundreds of builders to try replicating the setup. Which is when the problems started.
What Went Wrong
The replies and follow-up posts revealed significant issues that anyone running multi-agent systems should understand:
1. Escalating Costs
"The LLM bills are insane."
10 agents running 24/7, each making multiple LLM calls per task, adds up fast. Early adopters reported bills 10-50x higher than expected. Without cost controls, a single runaway discussion could burn through hundreds of dollars in API credits.
The fix: Per-agent budgets, cost caps, and model selection policies.
2. Quality Degradation Over Time
"After a few days, the agents start producing garbage."
Autonomous systems drift. Without human correction, small errors compound. Agents start hallucinating "facts" that other agents treat as ground truth. Circular discussions emerge where agents agree with each other without adding value.
The fix: Human review checkpoints, quality gates, and periodic context resets.
3. Single Point of Failure
"When Jarvis goes weird, everything breaks."
Over-reliance on the lead agent creates fragility. If Jarvis hallucinates a bad strategy or gets stuck in a loop, the entire squad follows it off the cliff.
The fix: Leadership rotation, consensus requirements for major decisions, and circuit breakers.
4. No Grounding to Reality
"The agents are productive but disconnected from actual revenue."
Activity isn't value. Agents can produce mountains of content, research, and code—but if it's not tied to business outcomes, you're just burning compute on busywork.
The fix: Define success metrics upfront. Measure output quality, not volume.
5. Debuggability Nightmare
"I have no idea why the agents decided to do that."
After days of autonomous operation, reconstructing the decision chain becomes nearly impossible. Why did the agents prioritize this task? What context led to that output? Without comprehensive logging, you're flying blind.
The fix: Full audit trails with searchable history and replay capability.
6. Security Vulnerabilities
"The agents have access to... everything."
Broad tool access is what makes agents useful. It's also what makes them dangerous. In Mission Control, agents could execute shell commands, access APIs, and modify files. A prompt injection or hallucinated command could cause real damage.
The fix: Sandboxed execution, tool allowlists, and human-in-the-loop approvals for high-risk actions.
The Core Problem: Production vs Proof-of-Concept
Mission Control is an inspiring proof-of-concept. It demonstrates that multi-agent collaboration works.
But running it as described—10 agents with broad access, operating autonomously 24/7—is not production-ready.
The missing pieces:
| Gap | Risk | Mitigation |
|---|---|---|
| No audit trail | Can't debug or explain agent behavior | Comprehensive logging |
| No cost controls | Runaway API bills | Per-agent budgets |
| No human oversight | Quality degradation, errors compound | Approval workflows |
| No sandboxing | Agents can access anything | Isolated containers |
| No kill switch | Can't stop runaway agents | One-command termination |
| No secrets management | API keys exposed | Encrypted vault |
How to Deploy Multi-Agent Systems Safely
If you want Mission Control-style productivity with production-grade safety, here's the approach:
1. Start Small, Add Agents Incrementally
Don't launch 10 agents on day one. Start with 2-3 core agents, validate their behavior, then expand. Each new agent increases complexity exponentially.
2. Define Clear Boundaries
Each agent should have:
- Explicit tool allowlist — Only the tools it needs
- Scope limitations — Can't touch resources outside its domain
- Budget caps — Maximum LLM spend per hour/day
3. Implement Human Checkpoints
Full autonomy sounds appealing until something goes wrong. Add approval requirements for:
- File deletions
- External API calls
- Financial transactions
- Database modifications
- Email/messaging sends
Clawctl blocks 70+ high-risk action types by default, requiring human approval before execution.
4. Log Everything
You need complete visibility into agent behavior:
- Every prompt and response
- Every tool call and result
- Every inter-agent communication
- Timestamps and context
This is non-negotiable for debugging and compliance.
5. Use Sandboxed Execution
Agents should run in isolated containers with:
- Restricted filesystem access
- Network egress controls (only approved domains)
- Resource limits (CPU, memory)
- No access to production credentials directly
6. Plan for Failure
Things will go wrong. Have ready:
- Kill switch — Stop all agents with one command
- Rollback capability — Undo agent actions
- Alert thresholds — Notify when behavior deviates from expected
Mission Control + Clawctl
Clawctl is a secure, managed runtime for OpenClaw that provides the missing production pieces:
| Mission Control Gap | Clawctl Solution |
|---|---|
| No audit trail | Full logging with 365-day retention, search, export |
| No cost controls | Per-agent budgets and usage limits |
| No human oversight | Human-in-the-loop approvals (70+ action types) |
| No sandboxing | Isolated Docker containers per agent |
| No kill switch | One-command agent termination |
| No secrets management | Encrypted vault with runtime injection |
| No debugging | Deterministic replay of agent runs |
You can deploy 25 agents on the Business plan, each with separate policies and audit trails, managed from a single dashboard.
Building Your Own Mission Control
Ready to try multi-agent orchestration? Here's the safe path:
Step 1: Deploy Your First Agent
Sign up at clawctl.com/checkout, pick a plan, and your agent is provisioned automatically in under 60 seconds.
Step 2: Configure Agent Roles
Define each agent's SOUL (personality, skills, allowed tools) in your OpenClaw config. Start with:
- One coordinator agent
- One specialist agent
- Narrow scope for both
Step 3: Enable Guardrails
From the Clawctl dashboard:
- Set up approval workflows for high-risk actions
- Configure network egress allowlists
- Define cost budgets per agent
Step 4: Monitor and Expand
Watch the audit logs. Understand the patterns. Add agents one at a time, only when you trust the existing setup.
Step 5: Scale with Confidence
Once you've validated the system:
- Add specialized agents for new domains
- Increase autonomy for trusted action types
- Extend retention for compliance
The Bottom Line
Mission Control proves that multi-agent AI systems can produce real value. The architecture is sound. The concept works.
But running it safely requires more than just spinning up 10 OpenClaw instances. You need:
- Audit trails for debugging and compliance
- Approval workflows for human oversight
- Sandboxed execution for security
- Cost controls for budget management
- Kill switches for emergencies
Clawctl provides all of this out of the box.
The question isn't whether to build multi-agent systems—it's how to deploy them without creating a liability.
Deploy your agent squad securely
FAQ
Q: How many agents can I run with Clawctl? Starter plan supports 1 agent. Team supports 5. Business supports 25. Enterprise is unlimited.
Q: Can I replicate Mission Control exactly? Yes, with proper guardrails. We recommend starting with 2-3 agents and expanding incrementally.
Q: What's the cost difference vs raw OpenClaw? Clawctl adds ~$49-299/month on top of your LLM costs, but prevents the runaway scenarios that can cost thousands. It's insurance.
Q: Can agents communicate with each other? Yes. OpenClaw supports inter-agent messaging. Clawctl logs all communications for audit purposes.
Inspired by @pbteja1998's Mission Control setup. Feedback? hello@mg.clawctl.com