PM Agent — AI Product Management Workflow
Four agents covering the full PM lifecycle: Research → Define → Validate → Launch. Each phase uses proven frameworks and produces structured artifacts. Human checkpoints between phases.
Phases
| # | Phase | Agent | Framework | Output |
|---|
| 1 | Research | Market & User Analyst | JTBD + Design Thinking | INLINECODE0 |
| 2 |
Define | Product Strategist | Opportunity Solution Tree + Amazon PRD |
PRD.md |
| 3 |
Validate | Experiment Designer | Design Sprint + Lean BML |
EXPERIMENT.md |
| 4 |
Launch | Go-to-Market Lead | Dual-Track Agile + OKR |
GTM.md |
How to Use
Full Workflow
CODEBLOCK0
Each phase spawns a focused subagent with the right prompt. The subagent asks questions, challenges assumptions, and produces a structured artifact.
Partial Workflow
- - "Just do a competitor analysis" → run Research only
- "Help me prioritize my backlog" → run Define (RICE/Kano section)
- "Write a PRD for this feature" → run Define with the feature description
- "Plan a design sprint" → run Validate only
- "Create a GTM plan" → run Launch only
Single Commands
- -
/research — JTBD interview analysis, market sizing, competitive landscape - INLINECODE5 — Opportunity Solution Tree, PRD with Amazon Working Backwards
- INLINECODE6 — Experiment design, prototype testing plan, BML metrics
- INLINECODE7 — GTM strategy, OKRs, release checklist
Phase Details
Phase 1: Research (JTBD + Design Thinking)
Goal: Understand the problem before proposing solutions.
Spawn a subagent (Sonnet) with the Research prompt from references/prompts.md. It will:
- 1. JTBD Analysis — Extract the "job" users are hiring the product for
- Push factors (pain with current solution)
- Pull factors (attraction of new solution)
- Trigger event (what moment starts the search)
- 2. Competitive Landscape — Map existing solutions and gaps
- Market Sizing — TAM/SAM/SOM with reasoning
- User Personas — 2-3 evidence-based personas (not fictional)
- Write
DISCOVERY.md — Consolidated research artifact
Key question: "What job is the user hiring this product to do?"
Phase 2: Define (Opp. Tree + Amazon PRD)
Goal: Define what to build and why, before how.
Spawn a subagent (Sonnet) with the Define prompt. It reads DISCOVERY.md and produces:
- 1. Opportunity Solution Tree — Visual hierarchy of outcome → opportunities → solutions
- Prioritization — RICE scoring for top opportunities, Kano classification
- Amazon PRD — Working Backwards: start with the press release, then FAQ
- User Stories — INVEST-compliant stories with acceptance criteria
- Write
PRD.md — Complete product requirements document
Key rule: No solution before opportunity. No feature before user story.
Phase 3: Validate (Design Sprint + Lean)
Goal: Test assumptions before building.
Spawn a subagent (Sonnet) with the Validate prompt. It reads PRD.md and produces:
- 1. Assumption Map — Classify by risk (lethality × uncertainty)
- Experiment Design — Lean BML cycle for riskiest assumptions
- Prototype Plan — What to mock up and how to test with 5 users
- Success Metrics — Quantitative pass/fail criteria per experiment
- Write
EXPERIMENT.md — Validation plan with test scripts
Key rule: Test the riskiest assumption first, not the easiest.
Phase 4: Launch (GTM + OKR)
Goal: Ship and measure.
Spawn a subagent (Haiku) with the Launch prompt. It reads PRD.md and EXPERIMENT.md and produces:
- 1. GTM Strategy — ICP, positioning, channel mix
- OKRs — 3 measurable objectives with key results
- Release Checklist — Pre-launch, launch day, post-launch tasks
- Feedback Loop — How to collect and act on user signals
- Write
GTM.md — Launch plan with timelines
Key rule: Launch is not the end. It's the beginning of the BML cycle.
Model Selection
| Phase | Model | Why |
|---|
| Research | Sonnet | Needs reasoning for market analysis |
| Define |
Sonnet | Strategic decisions require depth |
| Validate | Sonnet | Experiment design needs critical thinking |
| Launch | Haiku | Mostly structured execution |
Output Files
All phase outputs go to the project root:
- -
DISCOVERY.md — Research findings (JTBD, personas, competitive landscape) - INLINECODE18 — Product requirements (Opp. Tree, Amazon PRD, user stories)
- INLINECODE19 — Validation plan (assumptions, experiments, metrics)
- INLINECODE20 — Launch plan (GTM, OKRs, checklist)
Each file is self-contained but references previous phases. You can run phases independently by providing the prerequisite context.
Frameworks Reference
For detailed framework guides (JTBD interview templates, RICE calculators, Amazon PRD templates), see references/frameworks.md.
Human-in-the-Loop
Each phase ends with a checkpoint:
- - Approve — proceed to next phase as-is
- Edit — modify the artifact, then proceed
- Rerun — provide feedback, regenerate the phase
This mirrors real PM work: AI drafts, humans decide.
PM Agent — AI产品管理工作流
四个智能体覆盖完整的产品管理生命周期:研究→定义→验证→发布。每个阶段使用成熟的框架并生成结构化文档。阶段之间设置人工检查点。
阶段
| # | 阶段 | 智能体 | 框架 | 输出 |
|---|
| 1 | 研究 | 市场与用户分析师 | JTBD + 设计思维 | DISCOVERY.md |
| 2 |
定义 | 产品策略师 | 机会解决方案树 + 亚马逊PRD | PRD.md |
| 3 |
验证 | 实验设计师 | 设计冲刺 + 精益BML | EXPERIMENT.md |
| 4 |
发布 | 上市负责人 | 双轨敏捷 + OKR | GTM.md |
使用方法
完整工作流
我想构建[产品创意] → 运行全部4个阶段
对[问题陈述]运行pm-agent
每个阶段会生成一个带有正确提示词的聚焦子智能体。该子智能体会提出问题、挑战假设,并生成结构化文档。
部分工作流
- - 只做竞品分析 → 仅运行研究阶段
- 帮我排定待办事项优先级 → 运行定义阶段(RICE/Kano部分)
- 为这个功能写PRD → 使用功能描述运行定义阶段
- 规划设计冲刺 → 仅运行验证阶段
- 创建上市计划 → 仅运行发布阶段
单条指令
- - /research — JTBD访谈分析、市场规模、竞争格局
- /define — 机会解决方案树、亚马逊逆向工作法PRD
- /validate — 实验设计、原型测试计划、BML指标
- /launch — 上市策略、OKR、发布检查清单
阶段详情
阶段1:研究(JTBD + 设计思维)
目标: 在提出解决方案之前理解问题。
使用references/prompts.md中的研究提示词生成子智能体(Sonnet)。它将:
- 1. JTBD分析 — 提取用户雇佣产品完成的任务
- 推力因素(当前解决方案的痛点)
- 拉力因素(新解决方案的吸引力)
- 触发事件(什么时刻开始搜索)
- 2. 竞争格局 — 映射现有解决方案和差距
- 市场规模 — 总可寻址市场/可服务可寻址市场/可获取市场,附带推理
- 用户画像 — 2-3个基于证据的用户画像(非虚构)
- 编写DISCOVERY.md — 整合的研究文档
关键问题: 用户雇佣这个产品来完成什么任务?
阶段2:定义(机会树 + 亚马逊PRD)
目标: 在考虑如何构建之前,定义构建什么以及为什么构建。
使用定义提示词生成子智能体(Sonnet)。它读取DISCOVERY.md并生成:
- 1. 机会解决方案树 — 结果→机会→解决方案的可视化层级
- 优先级排序 — 对顶级机会进行RICE评分,Kano分类
- 亚马逊PRD — 逆向工作法:从新闻稿开始,然后是FAQ
- 用户故事 — 符合INVEST原则的用户故事及验收标准
- 编写PRD.md — 完整的产品需求文档
关键规则: 没有机会就没有解决方案。没有用户故事就没有功能。
阶段3:验证(设计冲刺 + 精益)
目标: 在构建之前测试假设。
使用验证提示词生成子智能体(Sonnet)。它读取PRD.md并生成:
- 1. 假设地图 — 按风险分类(致命性×不确定性)
- 实验设计 — 针对风险最高的假设的精益BML循环
- 原型计划 — 需要模拟什么以及如何与5个用户测试
- 成功指标 — 每个实验的定量通过/失败标准
- 编写EXPERIMENT.md — 包含测试脚本的验证计划
关键规则: 先测试风险最高的假设,而不是最简单的。
阶段4:发布(上市 + OKR)
目标: 交付并衡量。
使用发布提示词生成子智能体(Haiku)。它读取PRD.md和EXPERIMENT.md并生成:
- 1. 上市策略 — 理想客户画像、定位、渠道组合
- OKR — 3个可衡量的目标及关键结果
- 发布检查清单 — 发布前、发布日、发布后任务
- 反馈循环 — 如何收集和处理用户信号
- 编写GTM.md — 包含时间线的发布计划
关键规则: 发布不是终点。它是BML循环的开始。
模型选择
| 阶段 | 模型 | 原因 |
|---|
| 研究 | Sonnet | 需要市场分析推理能力 |
| 定义 |
Sonnet | 战略决策需要深度 |
| 验证 | Sonnet | 实验设计需要批判性思维 |
| 发布 | Haiku | 主要是结构化执行 |
输出文件
所有阶段输出都放在项目根目录:
- - DISCOVERY.md — 研究发现(JTBD、用户画像、竞争格局)
- PRD.md — 产品需求(机会树、亚马逊PRD、用户故事)
- EXPERIMENT.md — 验证计划(假设、实验、指标)
- GTM.md — 发布计划(上市、OKR、检查清单)
每个文件都是独立的,但会引用前一阶段的内容。你可以通过提供前置上下文来独立运行各个阶段。
框架参考
有关详细的框架指南(JTBD访谈模板、RICE计算器、亚马逊PRD模板),请参见references/frameworks.md。
人在循环中
每个阶段结束时都有一个检查点:
- - 批准 — 按原样进入下一阶段
- 编辑 — 修改文档,然后继续
- 重新运行 — 提供反馈,重新生成该阶段
这反映了真实的产品管理工作:AI起草,人类决策。