LaunchLens Research Studio
一个想法值不值得做，过去要请咨询公司花几周时间、十几万元来验证。Research Studio 派出 6 个 AI 角色并行调研——市场分析师扒规模、竞品专家拆解对手、用户研究员还原真实使用场景、风控顾问挑刺、PM 估算落地难度——5 分钟内把竞品、用户、风险三个维度同时拉齐到一份决策级简报里。整套调研链路可审计、可重跑、可对比不同假设下的结论差异。适合产品经理、战略分析师、早期投资人快速判断「这个方向能不能投、要不要做」。
A single long-context agent tends to blur market, competitor, persona, pricing, channel, and risk research into one fragile answer.
I split research into specialist agents, ran them in parallel, and merged their outputs into a structured brief so each dimension can fail, recover, and improve independently.
The result demonstrates bounded delegation, partial-failure tolerance, and faster decision loops for applied multi-agent research.
- 01Designed the fan-out/fan-in agent architecture.
- 02Built the schema-driven brief renderer and project workflow.
- 03Tuned the visible product language around evidence, not just automation.
Pushing a single agent to its limits is the wrong tradeoff — six small specialist agents running in parallel each finish in under 30 seconds, and the merge step produces a better brief than a single long-context call.
Fan-out / fan-in topology. A coordinator dispatches six research subagents (market, competitor, persona, pricing, channels, risks) that run concurrently with isolated contexts. Results merge into a structured GTM brief validated against a Zod schema.
- 01Parallel execution via Promise.allSettled — no subagent blocks another; partial failures degrade gracefully.
- 02Isolated context windows per agent — keeps token usage bounded and prevents cross-contamination of research threads.
- 03Brief renderer is fully schema-driven — adding a new research dimension means adding a Zod field, not a new component.