Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production
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 network...
| Authors | Arun Malik |
|---|---|
| Primary category | cs.SE |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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