让 AI 理解业务，需要可靠的知识；让 AI 推进业务，还需要可控的系统连接。
制度、产品说明和业务文档是智能体的重要依据。建设知识库时，应明确资料的负责人、版本和适用范围，让回答可以追溯到来源。
过期、冲突或权限不匹配的资料，会降低结果的可信度。知识更新和访问控制应与业务管理一起规划。
连接 ERP、CRM、OA 等系统后，智能体可以在授权范围内查询状态、整理数据或发起操作。不同系统的能力应被封装成明确的工具接口。
查询与写入应分别授权。对于不可逆或影响较大的操作，宜先展示待执行内容，再由有权限的人员确认。
工具调用应记录输入、执行结果和异常信息。出现问题时，团队需要能够定位是资料不足、规则不清，还是接口执行失败。
可观察、可回退、可接管的连接方式，能帮助团队逐步扩大自动化范围。
Traditional scripts rely on fixed rules, while enterprise AI agents understand requests, look up information, apply rules and call systems, handing exceptions back to people with a traceable, verifiable execution trail.
Prefer work with clear inputs and checkable results, such as consolidating purchase requests, suggesting ticket categories or tidying up sales follow-up notes. The more concrete the task, the easier it is to judge whether the agent genuinely improved the work.
When agents spread across departments, permissions, quality, cost and audit need to live in one management mechanism, so that quality, access and exception handling are all validated before replicating to more scenarios.
