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Progressive Crystallization Lowers LLM Inference Costs in AI Agent Workflows
The paper introduces progressive crystallization, a lifecycle approach that shifts AI agent workflows from constant full LLM inference to a staged execution model. This reduces costs by moving from fully agent-orchestrated to hybrid and eventually fully deterministic workflows.
AI agents in IT operations often incur high costs due to repeated full LLM inference. Progressive crystallization offers a method to lower these costs by treating agent exploration as a discovery phase rather than a permanent execution mode.
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Score 80
Source Type arxiv
Reposts 0
Topic Quality 61
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