Mission Control: How We Built an AI Agent Squad with OpenClaw

2026-05-15 · source · source ↗

Bhanu Teja (founder of SiteGPT.ai) posted a detailed thread on X describing how he built "Mission Control" — a 24/7 AI agent squad of 10 specialized agents running on OpenClaw (formerly clawdBot). The thread is widely cited as the best real-world OpenClaw multi-agent deployment.

Core Architecture

The 10 Agents

Agent Role Specialty
Jarvis Squad Lead Coordination, delegation, progress monitoring
Shuri Product Analyst Competitive analysis, skeptic mindset
Fury Customer Researcher Deep research, evidence-driven
Vision SEO Analyst Keywords, search intent, content structure
Loki Content Writer Copywriting, synthesizing inputs
Quill Social Media Manager Hooks, thread writing, distribution
Wanda Designer Visual thinking, graphics, infographics
Pepper Email Marketing Drip sequences, lifecycle emails
Friday Developer Implementation, clean tested documented code
Wong Documentation Manager Organizing docs, preventing knowledge loss

Memory System (5 Layers)

  1. Session Memory: JSONL conversation logs (searchable)
  2. Working Memory: WORKING.md — current task state, agent reads this first on wake
  3. Log Memory: YYYY-MM-DD.md — daily chronological event log
  4. Long-term Memory: MEMORY.md — curated key facts, decisions, lessons
  5. Identity Memory: SOUL.md / AGENTS.md — personality definition + operations manual

Core principle: "If you want to remember it, write it to a file."

Workflow

Kanban board with 6 stages: Inbox → Assigned → In Progress → Review → Done (or Blocked).

Three autonomy levels: Internal (most need approval), Specialist (independent within domain), Lead (can decide and delegate).

Heartbeat & Cost Optimization

Communication

Bhanu's Practical Advice

  1. Start with 2-3 agents, validate the memory→coordination→review loop, then expand
  2. Stagger heartbeats to avoid simultaneous wakeups
  3. UI is essential above 3 agents — logs and chat alone become unmanageable
  4. Least privilege: file and shell permissions per agent
  5. Human review is not optional — agents are force multipliers, not replacements
  6. Cheap models for routine, expensive models for creative work

Outputs

SEO competitive comparison pages, email marketing sequences, social media content, blog posts and case studies, competitive intelligence research hub.