How to Build Mission Control: Running 10 AI Agents as a Coordinated Team

A tweet from Bhanu Teja P broke the internet this week: "The Complete Guide to Building Mission Control: How We Built an AI Agent Squad."

6,562 likes. 2.72 million views. And a question everyone is asking: How do you actually run 10 AI agents that work together like a real team?

The answer isn't "just run more Clawdbots." The breakthrough comes from something most builders miss entirely: shared infrastructure.

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The Problem with Multiple Agents

Here's the naive approach everyone tries first: spin up ten OpenClaw instances, give each one tasks, and hope for the best.

The result? Chaos.

Agents duplicate work. They miss context. They produce inconsistent outputs. One agent researches a competitor while another writes content about a completely different angle—because neither knows what the other is doing.

Bhanu Teja's insight was simple: ten agents need the same infrastructure a ten-person team needs.

• A shared task database where everyone sees the same work
• Comment threads where agents discuss in one place
• An activity feed for real-time visibility
• @mentions to alert specific agents
• A shared repository for deliverables

This is Mission Control.

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Meet the Squad

Rather than one powerful AI assistant, the squad approach deploys specialists:

Jarvis (Squad Lead) — The coordinator. Handles direct requests, delegates to specialists, monitors progress.

Shuri (Product Analyst) — The skeptical tester. Finds edge cases and UX issues. Tests competitors. Asks questions others miss.

Fury (Customer Researcher) — The deep researcher. Reads G2 reviews. Backs every claim with receipts.

Vision (SEO Analyst) — Thinks in keywords and search intent. Ensures content can actually rank.

Loki (Content Writer) — Words are craft. Pro-Oxford comma. Anti-passive voice.

Each agent has an agents.md file defining their personality, constraints, and specific instructions. "Most agents.md files fail because they are too vague," GitHub's documentation warns. Specificity is everything.

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How It Actually Works

The Scheduling Trick

Agents don't run continuously. They wake on staggered 15-minute schedules:

Jarvis:  :00, :15, :30, :45
Shuri:   :03, :18, :33, :48
Fury:    :06, :21, :36, :51
Vision:  :09, :24, :39, :54

This prevents simultaneous API calls (rate limits), smooths costs (no spikes), and distributes resource load.

Each cron creates an isolated session that runs, does its job, and terminates. Cost-effective and clean.

The Task Flow

1. User assigns task → drags to ASSIGNED column
2. System dispatches → sends to agent's OpenClaw session
3. Agent works → task moves to IN PROGRESS
4. Agent completes → sends TASK_COMPLETE: [summary]
5. Auto-transition → task moves to REVIEW
6. Master approves → task moves to DONE

The magic is in step 6: only a master agent (Charlie by default) can approve tasks from REVIEW to DONE. This provides quality control without human bottlenecks.

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The Five Orchestration Patterns

Microsoft's research identifies five patterns for multi-agent systems. Choosing wrong affects costs by 200%+.

Sequential

Assembly line. Each agent processes the previous output.

Draft → Edit → Review → Publish

Best for: Clear linear workflows. Lowest token cost.

Concurrent

Multiple agents work simultaneously from different perspectives.

Best for: Independent analysis, time-sensitive scenarios. Medium cost.

Group Chat

Agents participate in shared conversation, collaborating through discussion.

Best for: Brainstorming, consensus. Limit to 3 agents max.

Handoff

Dynamic delegation—each agent decides to handle or transfer to a specialist.

Best for: Customer support routing, expertise-based delegation.

Magentic

Manager agent builds dynamic task ledger, coordinating tool-enabled specialists.

Best for: Complex, open-ended problems. Highest cost.

Most real implementations combine patterns: data processing (Sequential) → analysis (Concurrent) → review (Group Chat).

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The 5-Minute Rule

GitHub's research reveals a critical insight: early course correction within 5 minutes of launch prevents hours of wasted work.

Monitor for warning signs:

• Failing tests or integrations
• Files outside intended scope in diffs
• Session logs showing misunderstood intent
• Circular behavior (retrying failed approaches)

Intervention after the 5-minute window often means starting over. Session logs are your early warning system.

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Getting Started

Step 1: Choose your stack

The open-source Mission Control uses Next.js, SQLite, and Zustand. It connects to OpenClaw Gateway via WebSocket. Clone it from GitHub.

Step 2: Define 2-3 agents

Start small. Create agents.md files with:

• Clear role (what they own)
• Distinct perspective (how they approach problems)
• Specific instructions (exact patterns to follow)
• Constraints (what they should NOT do)

Step 3: Set up shared infrastructure

• Task database (SQLite works)
• Activity feed for visibility
• Comment threads for discussion
• Document storage for deliverables

Step 4: Implement staggered schedules

Cron jobs every 15 minutes, offset by 3 minutes per agent.

Step 5: Add master approval

Quality control without human bottlenecks.

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The Numbers

The market is moving fast:

• 40% of enterprise apps will feature AI agents by 2026 (up from 5% in 2025)
• $450B projected economic value by 2028
• 2% of organizations have deployed at full scale today

The builders implementing Mission Control patterns now will have 12-18 months head start on enterprises waiting for "ready-made" solutions.

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The Bottom Line

Ten agents, working as a team, accomplish what no single agent can alone.

The infrastructure exists. The patterns are documented. The open-source tools are available.

The only question is: are you building?

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Based on Bhanu Teja P's viral guide and research from Microsoft, GitHub, and AWS on multi-agent orchestration patterns.