将智能体探索转化为低成本确定性流程，提升运维效率与安全性
Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production
- 用三阶段流程把反复验证的智能体行为转为低成本确定性工作流
- 上线8个月后确定性执行占比达45%，单次故障成本降低70%以上
- 适合追求生产系统稳定性与降本的AI运维团队参考
部署于IT运维的AI智能体通常为持续成本中心，每次执行均需完整大模型推理，即使问题已解决。本文提出渐进结晶（Progressive Crystallization）机制，将智能体探索视为发现过程而非永久执行模式。定义从全智能体编排到混合再到完全确定性流程的三阶段执行范式，并建立基于证据的晋升机制，将多次验证的智能体行为转化为更廉价、可复现的确定性工作流，同时自动降级出现退化的流程。在处理每月数万起事件的生产级云网络AIOps系统上评估，八个月内确定性执行比例从0%提升至45%，尽管事件量翻倍，单次事件智能体成本仍下降超过70%，且通过更高可复现性与可审计性增强了安全性。论文还提供了执行范式、晋升/降级标准、轨迹提取方法、经济模型及安全考量，并讨论了局限性与有效性威胁。
原文摘要 · Abstract (English)
AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems. This paper introduces progressive crystallization, a lifecycle that treats agent exploration as a discovery mechanism rather than a permanent execution model. It defines a three-stage execution taxonomy, from fully agent-orchestrated to hybrid to fully deterministic workflows, together with an evidence-based promotion mechanism that converts repeatedly validated agent behaviors into cheaper and more reproducible deterministic workflows, while automatically demoting workflows that regress. Evaluated on a production cloud networking AIOps system processing tens of thousands of incidents per month, the approach increased deterministic execution from 0% to 45% over eight months, reduced per-incident agent costs by more than 70% despite doubling incident volume, and improved safety through greater reproducibility and auditability. The paper also presents the execution taxonomy, promotion and demotion criteria, trace extraction methodology, economic model, safety considerations, and discusses limitations and threats to validity.
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