[{"data":1,"prerenderedAt":364},["ShallowReactive",2],{"docs-doc-\u002Fdocs\u002Fconcepts\u002Fai":3,"docs-live-paths":314},{"id":4,"title":5,"body":6,"description":300,"extension":301,"meta":302,"navigation":309,"path":310,"seo":311,"stem":312,"__hash__":313},"content\u002Fdocs\u002Fconcepts\u002Fai.md","AI 数字员工：工作机制与能力边界",{"type":7,"value":8,"toc":284},"minimark",[9,16,20,32,35,38,41,46,49,53,60,64,67,71,125,129,132,164,168,175,186,194,197,227,230,242,253,256],[10,11,12],"blockquote",{},[13,14,15],"p",{},"本文面向希望了解 AI 数字员工技术构成的读者（实施者、技术评估方）。它拆解「查数、汇总、拟稿、录单、做报表」这类任务在平台内如何被自动执行，以及其中的约束与边界。业务使用方只需知道：这类可重复的执行能力由平台内置，无需额外自建。",[17,18,19],"h2",{"id":19},"一句话定义",[13,21,22,23,27,28,31],{},"衍数的 ",[24,25,26],"strong",{},"AI 数字员工","，是能进入业务系统",[24,29,30],{},"执行任务","的一组 AI 能力：它们可在授权范围内调用数据库与接口完成查询、汇总、拟稿、录入等操作。页面右下角的「在线咨询」即一名实际运行的数字员工。",[13,33,34],{},"与其对应的常见形态是独立问答式 AI（仅提供对话应答、不进入业务数据）。衍数的定位是将 AI 嵌入具体业务动作，并置于授权与审计约束之下——这一定位差异是理解后续机制的前提。",[17,36,37],{"id":37},"三个组成",[13,39,40],{},"AI 数字员工不是单个模型，而是三部分协同：",[42,43,45],"h3",{"id":44},"_1-agent任务执行层","1. Agent：任务执行层",[13,47,48],{},"Agent 负责把「一句自然语言指令」拆解为「一系列可执行操作」：查询数据 → 判断结果 → 决定下一步。其操作范围严格受权限约束——可读取哪些数据、可改动哪些数据，均由平台设定。",[42,50,52],{"id":51},"_2-知识库让-ai-基于企业资料作答","2. 知识库：让 AI 基于企业资料作答",[13,54,55,56,59],{},"通用大模型不具备企业特有知识。衍数通过知识库让 AI 基于",[24,57,58],{},"企业自身资料","回答：将制度、产品资料、业务文档纳入知识库后，AI 先检索资料再作答，答案有据可查，而非凭空生成。实现方式为检索增强生成（RAG）。",[42,61,63],{"id":62},"_3-统一模型网关模型可替换不锁定单一供应商","3. 统一模型网关：模型可替换、不锁定单一供应商",[13,65,66],{},"平台通过统一网关接入 DeepSeek、通义千问等多家模型。可按任务类型选择模型（问答类、推理类使用不同侧重），避免 AI 能力与单一供应商的账号、定价绑定。",[17,68,70],{"id":69},"员工与助手两种使用形态","员工与助手：两种使用形态",[72,73,74,88],"table",{},[75,76,77],"thead",{},[78,79,80,83,85],"tr",{},[81,82],"th",{},[81,84,26],{},[81,86,87],{},"AI 助手",[89,90,91,103,114],"tbody",{},[78,92,93,97,100],{},[94,95,96],"td",{},"承担的任务",[94,98,99],{},"承接被派发的具体任务（拟稿、查数、录单、做报表）",[94,101,102],{},"在场景内就地解答（数据解读、录入提示）",[78,104,105,108,111],{},[94,106,107],{},"形态",[94,109,110],{},"有独立职责，可被 @ 指派任务",[94,112,113],{},"内嵌于页面，随用随取",[78,115,116,119,122],{},[94,117,118],{},"共性",[94,120,121],{},"均受统一权限约束、行为可审计",[94,123,124],{},"同左",[17,126,128],{"id":127},"能力边界理性说明","能力边界（理性说明）",[13,130,131],{},"衍数在 AI 能力上追求「可解释、可约束」，以下几点如实说明：",[133,134,135,142,152,158],"ul",{},[136,137,138,141],"li",{},[24,139,140],{},"结论有据","：涉及数字的结论会引用来源，不提供无出处的数据。",[136,143,144,147,148,151],{},[24,145,146],{},"报告数字可靠","：生成报告或做数据解读时，AI 只把系统",[24,149,150],{},"按口径与筛选条件确定性算出的数据","组织成文字——数字来自查询、不来自生成，每一个数值都可回溯核对。这是报告可靠的依据：数据是系统算出来的，AI 只负责把话说清楚。",[136,153,154,157],{},[24,155,156],{},"操作受约束","：AI 仅能在其角色权限内行动，越权请求被拒绝；关键操作留审计。",[136,159,160,163],{},[24,161,162],{},"非万能","：AI 适合规则明确、过程重复的任务；涉及业务判断的决策，由 AI 准备事实与依据，决定权在企业方。",[17,165,167],{"id":166},"报告是怎么生成的数字可靠链路","报告是怎么生成的（数字可靠链路）",[13,169,170,171,174],{},"AI 生成报告、做数据解读时，遵循「",[24,172,173],{},"数据先算、文字后写、逐字可查","」，而不是让 AI 凭空写数字：",[176,177,182],"pre",{"className":178,"code":180,"language":181},[179],"language-text","需求（要一份什么报告）\n   │\n   ▼\n① 系统按汇报范围取数\n   过滤条件 · 时间范围 · 指标口径 · 统计方式\n   都来自企业已配置的报表，而非 AI 自己猜\n   │\n   ▼\n② 得到「事实集」\n   每个数字都带出处（来自哪张报表、什么口径），由系统锁定\n   │\n   ▼\n③ AI 只负责组织文字\n   把事实写成读得懂的话；数字只能用「引用」带入，不允许自己编\n   │\n   ▼\n④ 系统逐字校验\n   报告里每个数字都要能在事实集里找到；找不到的剔除，宁缺勿错\n   │\n   ▼\n报告 = 系统算出的数据 + AI 组织的话\n   （逐字可回溯，可对照企业报表核验）\n","text",[183,184,180],"code",{"__ignoreMap":185},"",[133,187,188,191],{},[136,189,190],{},"关键指标卡、图表数据同样由系统按口径取数，不由 AI 生成后填数。",[136,192,193],{},"所以报告里的数字，和企业同一张报表上对得上的数字一致；AI 的价值在「把话讲清楚」，不在「替你算数」。",[17,195,196],{"id":196},"数据安全",[133,198,199,205,215,221],{},[136,200,201,204],{},[24,202,203],{},"数据不出外网","：私有化部署时，业务数据与提问均在企业环境内处理、不出企业网络，无需上传至公共云端。",[136,206,207,210,211,214],{},[24,208,209],{},"AI 接触面受控","：报告与数据解读中，AI 只接收系统",[24,212,213],{},"按授权口径取出的结果","，不接触企业全量原始数据——AI 既算不出、也带不走范围外的数据。",[136,216,217,220],{},[24,218,219],{},"回答有据可查","：涉及数字与结论的回答会标注并引用来源，可随时回溯核对；关键操作留存审计——数据流向可解释。",[136,222,223,226],{},[24,224,225],{},"模型接入由企业掌控","：即便接入外部大模型，也由企业选定接入方、按需开放范围并管理调用，可随时切换或断开——不存在模型供应商默认可取企业数据的情况。",[17,228,229],{"id":229},"常见疑问",[13,231,232,235,236,241],{},[24,233,234],{},"官网右下角的客服也算数字员工吗？","\n是的。它是衍数平台对外提供咨询的 AI 助手——可解答产品问题，复杂需求转人工对接。如需体验其在业务中的执行能力，可在",[237,238,240],"a",{"href":239},"\u002Fdocs\u002Fquickstart\u002Fdemo-30s","在线演示","中体验演示系统，或由右下角 AI 引导进入对应行业实例。",[13,243,244,247,248,252],{},[24,245,246],{},"能为自己的系统新增数字员工吗？","\n可以。AI 员工的创建、权限与任务派发均以配置方式提供，实施者可按业务场景配置（具体方法见",[237,249,251],{"href":250},"\u002Fdocs\u002Fguide\u002Fai-kb","使用指南 · 给 AI 配置知识库","）。",[17,254,255],{"id":255},"相关阅读",[133,257,258,265,271,278],{},[136,259,260,261],{},"想了解 AI 员工的整体组织与使用 → ",[237,262,264],{"href":263},"\u002Fdocs\u002Fconcepts\u002Fai-team","AI 数字员工体系 · 认识与使用",[136,266,267,268],{},"想看实际运行示例 → ",[237,269,270],{"href":239},"30 秒在线体验",[136,272,273,274],{},"想理解平台整体结构 → 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