人工智能代理越来越多地由非工程用户通过低代码,、无代码, 和对话式开发环境在组织内部创建。这种民主化实现了快速本地创新,，但也造成了可靠性差距: 代理在用户看来是简单的生产力工件，可能依赖于不断变化的模型, 工具, 检索源, 权限, 提示, 计划, 和外部服务。即使没有用户直接修改代理，这些依赖项也可能在部署, 后很长时间内导致静默降级。本文指出了民主化人工智能代理创建带来的可靠性挑战，并为公民创建的组织代理提出了一个轻量级的持续保证框架。该框架结合了依赖关系映射, 就绪合同, 计划检查, 诊断, 和生命周期治理，以评估代理是否在预期条件下保持运行就绪状态。我们还提出了初始原型审核员和基于场景的评估，展示了如何将拟议的分类法转化为实际检查和可行的补救指南。
AI agents are increasingly created inside organizations by non-engineering users through low-code, no-code, and conversational development environments. This democratization enables rapid local innovation, but it also creates a reliability gap: agents that appear to users as simple productivity artifacts may depend on changing models, tools, retrieval sources, permissions, prompts, schedules, and external services. These dependencies can cause silent degradation long after deployment, even when no user directly modifies the agent. This paper identifies the reliability challenge created by democratized AI agent creation and proposes a lightweight continuous-assurance framework for citizen-created organizational agents. The framework combines dependency mapping, readiness contracts, scheduled checks, diagnostics, and lifecycle governance to assess whether an agent remains operationally ready under expected conditions. We also present an initial prototype auditor and scenario-based assessment showing how the proposed taxonomy can be translated into practical checks and actionable remediation guidance.
主题: 人工智能 (cs.AI); 新兴技术 (cs.ET); 多代理系统 (cs.MA)
Subjects: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Multiagent Systems (cs.MA)