Multi Agent Orchestrator

Design, build, and manage multi-agent teams that turn a solo operator into a 1000-person organization. Covers team role design (CEO/PM/Engineer/Analyst/Writer/Monitor), inter-agent communication patterns, task distribution, conflict resolution, and priority management. Based on real production experience running 13-agent teams on OpenClaw. Use when the user wants to build an agent team, orchestrate multiple AI agents, design agent roles, set up agent communication, create a one-person company with AI agents, implement multi-agent workflows, or scale from solo to team-of-agents. Triggers on multi-agent, agent team, agent orchestration, AI team, agent collaboration, agent roles, one-person company, solo founder scaling, agent communication, task delegation to agents, swarm intelligence.

Build production-grade agent teams. Battle-tested with 13 concurrent agents running real operations—financial analysis, content production, code engineering, and executive decision-making.

Core Philosophy

One human, many agents. Each agent has a narrow, well-defined role. Communication flows through structured channels. The human sets direction; agents handle execution.

Three laws of agent teams:

1. Specialization over generality — Each agent does ONE thing well
2. Structured communication — No ad-hoc messaging; use defined protocols
3. Human-in-the-loop for decisions — Agents execute, humans decide

Workflow

Step 1: Define Your Team Composition

Read references/team-roles.md to select roles from the template library.

Start minimal (3 agents), scale to what you need:

Team Size	Composition	Use Case
Solo+1	Human + 1 Assistant	Personal productivity boost
Core (3)	CEO + PM + Engineer	Small project delivery
Full (5+)	+ Analyst + Writer + Monitor	Business operations
Enterprise (8+)	+ Specialist roles per domain	Multi-domain scaling

Step 2: Design Communication Architecture

Read references/communication-patterns.md for protocol details.

Choose your communication stack:

Pattern	Latency	Best For
Shared directory	Minutes	Async handoffs, document sharing
Message passing (sessions_send)	Seconds	Real-time coordination
Heartbeat scheduling	Periodic	Monitoring, health checks
Event queue	Async	Complex multi-step workflows

Step 3: Set Up Task Management

Read references/task-management.md for tracking systems and report formats.

Implement the three-layer task system:

1. Strategic (CEO layer) — Goals and OKRs
2. Tactical (PM layer) — Sprint planning and allocation
3. Operational (Engineer layer) — Ticket execution and delivery

Step 4: Launch and Iterate

bash

# Minimum viable team setup
1. Create AGENTS.md for each agent with role + scope
2. Set up shared directory structure
3. Define reporting cadence
4. Run first task cycle
5. Review and adjust roles/communication

Quick-Start Templates

Solo Founder Starter Pack (3 agents)

CEO (Strategic) — Owns vision, priorities, decision gates PM (Coordination) — Translates goals into tasks, tracks progress Engineer (Execution) — Builds, tests, ships

Content Machine (4 agents)

Strategist — Topic research, audience analysis, content calendar Writer — Draft production, SEO optimization Editor — Quality review, brand voice consistency Publisher — Distribution, scheduling, performance tracking

Research & Analysis (4 agents)

Data Collector — Gather raw data from sources Analyst — Process, analyze, identify patterns Synthesizer — Produce actionable reports Monitor — Track changes, alert on anomalies

Anti-Patterns (Learn from Our Mistakes)

Anti-Pattern	Why It Fails	Fix
Agent with 5+ responsibilities	Context thrashing, low quality	Split into specialized agents
All-to-all communication	Message chaos, conflicts	Hub-and-spoke or hierarchical
No escalation path	Decisions stall, work stops	Define clear human escalation triggers
Copy-paste between agents	Knowledge silos, inconsistency	Shared knowledge base or wiki
Agents making strategic decisions	Misaligned with human intent	Agents propose, humans dispose

Platform Compatibility

This skill is platform-agnostic. The principles apply to:

• OpenClaw — Native multi-agent with sessions_send, shared dirs, cron
• Claude Code — Multiple sessions with file-based coordination
• Codex — Task files and workspace sharing
• Cursor — Agent rules files (.cursor/rules/) with workspace coordination

Real-World Case Study

晟瑞智联 AI Team (13 agents, OpenClaw)

text

                    ┌─────────┐
                    │   于哥   │  (Human CEO)
                    └────┬────┘
                         │
                    ┌────┴────┐
                    │  老A    │  (CEO Agent / Strategic)
                    │  Friday │  (Executive Assistant)
                    └────┬────┘
                         │
          ┌──────────────┼──────────────┐
          │              │              │
     ┌────┴────┐   ┌────┴────┐   ┌────┴────┐
     │  艾琳   │   │  瓦里斯  │   │  唐    │
     │ (PM)    │   │(Engineer)│   │(Writer) │
     └────┬────┘   └────┬────┘   └────┬────┘
          │              │              │
     ┌────┴────┐   ┌────┴────┐   ┌────┴────┐
     │Simons   │   │  小E    │   │  +6     │
     │(Analyst)│   │(Monitor)│   │specialists│
     └─────────┘   └─────────┘   └─────────┘

Communication: Shared directories for async, sessions_send for real-time, cron-based heartbeat for monitoring.

References

• Team Roles: references/team-roles.md — Complete role templates with AGENTS.md examples
• Communication: references/communication-patterns.md — Protocols, file structures, message formats
• Task Management: references/task-management.md — Tracking systems, report formats, escalation rules