当智能体走向多个部门，权限、质量、成本与审计需要进入同一套管理机制。
智能体应只获得完成任务所需的访问权限。人员身份、业务角色和数据范围需要共同决定它能看到什么、能执行什么。
权限管理不应只停留在入口。知识检索、工具调用与结果输出都应遵循相同的访问边界。
模型选择需要结合任务难度、响应时间与使用成本。可通过固定的评估任务检查效果，并持续观察生产环境中的失败与人工修正情况。
成本监测可以按部门、工作流和模型拆分，帮助团队找到重复调用或不必要的复杂步骤。
为智能体设置负责人、发布审批与回滚机制。重要流程上线前，应验证异常输入、接口失败和人工接管等情况。
治理的目标是让业务可以持续改进：哪些任务适合自动完成，哪些需要确认，以及哪些暂时应由人员处理。
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.
