Fuzzy Multi Agent Team

Spawn and orchestrate multiple coordinated AI sub-agents to work in parallel on a single complex task. Use when: (1) a task is too large for one agent and should be decomposed into parallel subtasks, (2) you need multiple specialized agents researcher coder reviewer etc working together, (3) running agent councils or debates for decision-making, (4) parallel web research data processing or content generation across multiple workers, (5) any multi-agent orchestration pattern one-shot teams persistent squads or hierarchical agent trees. Triggers on phrases like spin up agents, spawn a team, parallel agents, agent council, multi-agent, coordinated agents.

Multi-Agent Team

Spawn, coordinate, and manage multiple AI sub-agents that work together on complex tasks. One agent is the orchestrator — it decomposes the task, assigns roles, collects results, and synthesizes the final output.

Patterns

Pattern 1: Disposable Team (one-shot)

Spawn multiple agents for a single task, collect results, done. Best for parallel research, generation, or data processing.

text

sessions_spawn(task="<task prompt>", runtime="subagent", mode="run")

Each agent gets a unique session. Results are auto-announced to the parent.

Pattern 2: Persistent Squad (ongoing collaboration)

Spawn agents with mode="session" so they maintain context across multiple interactions. Use sessions_send to message them and sessions_list to track who's active.

Pattern 3: Agent Council (debate/decision)

Spawn 3-5 agents with different perspectives/prompts, have each produce an analysis, then synthesize into a decision. Use sessions_yield to wait for all results.

Pattern 4: Hierarchical (orchestrator + workers)

One orchestrator agent decomposes the task and spawns worker sub-agents for each subtask, then collects and merges results.

Spawning Agents

json

sessions_spawn(
  task="You are a researcher agent. Research <topic> and return findings as a structured markdown summary.",
  runtime="subagent",
  runTimeoutSeconds=300,
  mode="run"  // or "session" for persistent
)

Key parameters:

• runtime="subagent" — spawn as OpenClaw sub-agent
• mode="run" — one-shot, exits when done
• mode="session" — persistent, stays alive for multiple interactions
• runTimeoutSeconds — kill after N seconds (0 = no timeout)
• task — the full agent prompt/instruction

Communicating with Agents

json

sessions_send(sessionKey="<key>", message="Update: the requirements changed to X, please adjust your approach.")
sessions_list(kinds=["subagent"], activeMinutes=60)  // find active agents
sessions_history(sessionKey="<key>", limit=10)  // read their recent messages

Collecting Results

Option A — Auto-announce: sub-agents announce results automatically (default).

Option B — Blocking wait: use sessions_yield to wait for sub-agent results before continuing:

json

sessions_yield(message="Waiting for research agents to report back...")

Option C — Poll history: after agents complete, fetch results:

json

sessions_history(sessionKey="<agent-session-key>", limit=20)

Orchestrator Template

When receiving a complex task, follow this sequence:

text

1. Decompose task into N independent subtasks
2. For each subtask, spawn a sub-agent with sessions_spawn(mode="run")
3. Optionally use sessions_yield to wait for results
4. Collect outputs from each agent session via sessions_history
5. Synthesize findings into a unified response
6. Report back to the parent session

Example orchestrator prompt:

text

You are a team orchestrator. The user wants: <task>

Step 1: Break this into 3-5 independent subtasks
Step 2: Spawn research/coder/writer agents for each
Step 3: Wait for all results via sessions_yield
Step 4: Merge into one coherent output
Step 5: Present the final result

Start by decomposing the task and spawning the first wave of agents.

Coordination Patterns

Fan-Out (parallel map)

Spawn N agents, each doing the same operation on different data:

text

Agent 1: process(item=A)
Agent 2: process(item=B)
Agent 3: process(item=C)
→ Merge results

Fan-In (gather)

Spawn agents that each contribute a piece, then one agent merges:

text

Agent 1: write introduction
Agent 2: write section A
Agent 3: write section B
Agent 4: write conclusion
→ Synthesis agent combines all sections

Sequential Pipeline

Each agent's output becomes the next agent's input:

text

Agent 1: research topic → findings
Agent 2: analyze findings → insights
Agent 3: write article based on insights → draft

Team Memory

For persistent squads, maintain shared context via files:

json

sessions_send(sessionKey="<orchestrator-key>", message="Update the team status in /workspace/team-status.md — mark task-2 as COMPLETE and note the findings.")

Workers can read/write to shared workspace files for state.

Cleanup

Use subagents(action="list") to find and kill stale agents:

json

subagents(action="kill", target="<session-key>")

Anti-Patterns

• Don't spawn 50 agents at once — the system may become unresponsive. Batch into waves of 3-5.
• Don't forget to collect results — agents that run to completion without reporting back waste their output.
• Don't use mode=session unless needed — persistent agents accumulate context and cost tokens. Use run for one-shot tasks.
• Don't spawn without a clear role — each agent needs a specific, focused prompt, not a vague "help me".

See Also

• agent-orchestrator skill — skill-level orchestration (not task-level)
• agent-council skill — decision-making with agent debates
• subagent-spawn-command-builder skill — helper for constructing spawn commands