为此,智能体工程通常通过以下几类手段给大模型加“外骨骼”以改善可靠性:引入检索与知识库(RAG)以降低幻觉和知识陈旧的影响;预先设计和约束工作流,而不是完全自由的“自治智能体”,以此限定可接受的执行路径;通过多次回答、自一致性检查或模型间交叉验证,识别并过滤高风险输出;在关键链路节点上设置人工审批,让人类对高风险动作“最后拍板”。
For reinforcement learning training pipelines where AI-generated code is evaluated in sandboxes across potentially untrusted workers, the threat model is both the code and the worker. You need isolation in both directions, which pushes toward microVMs or gVisor with defense-in-depth layering.
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