reddit · SaaS · 09-15 02:50 47 分 ← 返回列表

We thought the hard part of building agents was reasoning. Increasingly I think it’s capture.

We’re building agents that maintain operational state from updates people give them. Initially, Capture seemed like the easy part. Give people a text box → send the update to the agent → structure it. But the more autonomous the agents become, the more we’ve realised that bad state often starts before the agent ever reasons about anything. Humans don’t communicate like databases. Someone writes: “JD should have api done next fri. testing monday. let ralph know.” There’s shorth

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AI 分析

痛点：Agent 从人类自然语言更新中捕获并维护操作状态时，因简写、相对日期、隐含实体和冲突信息导致状态模糊，影响后续推理。

目标用户：构建自主 agent/工作流自动化产品的开发者与团队；需要从 Slack/邮件/文本维护项目状态的 B2B SaaS 团队。

证据：Don’t make humans structure information for software. Let humans communicate naturally, but clean up ambiguity as close to the source as possible. ... if the new information conflicts with what the system already understands, we don’t want the model silently choosing which version is true. Capture can surface the conflict and ask.

变现：按 API 调用/坐席订阅提供 Capture-as-a-Service：自然语言更新解析、实体/日期归一、冲突检测与询问；或作为 agent 框架的付费插件/企业版模块。

MVP：做一个 2 周 MVP：接入 Slack/邮件 webhook，用 LLM 将非结构化更新解析为项目状态（负责人、任务、截止日期、依赖），检测与现有状态的冲突并生成确认问题；输出 JSON/API 供 agent 调用。

竞品：Mem0、Zep、LangChain/LangGraph memory、Notion AI、Asana AI

摘要：发帖团队在构建维护操作状态的 agent 时发现，困难不在推理而在 capture：人类用简写、相对日期、隐含实体和冲突信息交流，直接传给 agent 会从源头引入歧义。他们主张在 capture 层做实体/日期归一和冲突询问，这指向一个面向 agent 开发者的自然语言状态捕获基础设施机会，但原帖没有付费意愿或收入数据。