← 返回
AI智能 中文

Agent Team Orchestration 1

Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with...
编排多智能体团队,定义角色、任务生命周期、交接协议与审查工作流。适用场景:(1) 组建2个及以上智能体团队时...
nuradil
AI智能 clawhub v1.0.0 1 版本 99874.4 Key: 无需
★ 0
Stars
📥 795
下载
💾 27
安装
1
版本
#latest

概述

Agent Team Orchestration

Production playbook for running multi-agent teams with clear roles, structured task flow, and quality gates.

Quick Start: Minimal 2-Agent Team

A builder and a reviewer. The simplest useful team.

1. Define Roles

Orchestrator (you) — Route tasks, track state, report results
Builder agent     — Execute work, produce artifacts

2. Spawn a Task

1. Create task record (file, DB, or task board)
2. Spawn builder with:
   - Task ID and description
   - Output path for artifacts
   - Handoff instructions (what to produce, where to put it)
3. On completion: review artifacts, mark done, report

3. Add a Reviewer

Builder produces artifact → Reviewer checks it → Orchestrator ships or returns

That's the core loop. Everything below scales this pattern.

Core Concepts

Roles

Every agent has one primary role. Overlap causes confusion.

RolePurposeModel guidance
------------------------------
OrchestratorRoute work, track state, make priority callsHigh-reasoning model (handles judgment)
BuilderProduce artifacts — code, docs, configsCan use cost-effective models for mechanical work
ReviewerVerify quality, push back on gapsHigh-reasoning model (catches what builders miss)
OpsCron jobs, standups, health checks, dispatchingCheapest model that's reliable

Read references/team-setup.md when defining a new team or adding agents.

Task States

Every task moves through a defined lifecycle:

Inbox → Assigned → In Progress → Review → Done | Failed

Rules:

  • Orchestrator owns state transitions — don't rely on agents to update their own status
  • Every transition gets a comment (who, what, why)
  • Failed is a valid end state — capture why and move on

Read references/task-lifecycle.md when designing task flows or debugging stuck tasks.

Handoffs

When work passes between agents, the handoff message includes:

  1. What was done — summary of changes/output
  2. Where artifacts are — exact file paths
  3. How to verify — test commands or acceptance criteria
  4. Known issues — anything incomplete or risky
  5. What's next — clear next action for the receiving agent

Bad handoff: "Done, check the files."

Good handoff: "Built auth module at /shared/artifacts/auth/. Run npm test auth to verify. Known issue: rate limiting not implemented yet. Next: reviewer checks error handling edge cases."

Reviews

Cross-role reviews prevent quality drift:

  • Builders review specs — "Is this feasible? What's missing?"
  • Reviewers check builds — "Does this match the spec? Edge cases?"
  • Orchestrator reviews priorities — "Is this the right work right now?"

Skip the review step and quality degrades within 3-5 tasks. Every time.

Read references/communication.md when setting up agent communication channels.

Read references/patterns.md for proven multi-step workflows.

Reference Files

FileRead when...
-------------------
team-setup.mdDefining agents, roles, models, workspaces
task-lifecycle.mdDesigning task states, transitions, comments
communication.mdSetting up async/sync communication, artifact paths
patterns.mdImplementing specific workflows (spec→build→test, parallel research, escalation)

Common Pitfalls

Spawning without clear artifact output paths

Agent produces great work, but you can't find it. Always specify the exact output path in the spawn prompt. Use a shared artifacts directory with predictable structure.

No review step = quality drift

"It's a small change, skip review." Do this three times and you have compounding errors. Every artifact gets at least one set of eyes that didn't produce it.

Agents not commenting on task progress

Silent agents create coordination blind spots. Require comments at: start, blocker, handoff, completion. If an agent goes silent, assume it's stuck.

Not verifying agent capabilities before assigning

Assigning browser-based testing to an agent without browser access. Assigning image work to a text-only model. Check capabilities before routing.

Orchestrator doing execution work

The orchestrator routes and tracks — it doesn't build. The moment you start "just quickly doing this one thing," you've lost oversight of the rest of the team.

When NOT to Use This Skill

  • Single-agent setups — Just follow standard AGENTS.md conventions. Team orchestration adds overhead that solo agents don't need.
  • One-off task delegation — Use sessions_spawn directly. This skill is for sustained workflows with multiple handoffs.
  • Simple question routing — If you're just forwarding a question to a specialist, that's a message, not a workflow.

This skill is for sustained team workflows — recurring collaboration patterns where agents depend on each other's output over multiple tasks.

版本历史

共 1 个版本

  • v1.0.0 当前
    2026-03-30 18:29 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

🔗 相关推荐

developer-tools

Browser Automation 1

nuradil
使用 agent-browser CLI 自动化网页浏览任务,包括页面导航、数据提取、表单填写、点击操作和截图。
★ 0 📥 1,029
ai-intelligence

ontology

oswalpalash
类型化知识图谱,用于结构化智能体记忆与可组合技能。支持创建/查询实体(人员、项目、任务、事件、文档)及关联...
★ 710 📥 243,666
ai-intelligence

self-improving agent

pskoett
捕获经验教训、错误和纠正,以实现持续改进。使用时机:(1)命令或操作意外失败;(2)用户纠正……
★ 4,058 📥 797,759