The cost of running AI agents is primarily determined by how often tasks repeat, rather than the per-token price of the model itself. A detailed cost breakdown of a 20-step agent task using Sonnet-5.5 revealed that re-sent context at each step significantly inflates costs, especially when agents enter retry loops. Strategies like trajectory caching, which replays recorded plans instead of re-deriving them, can drastically reduce expenses by serving repeated goals from a cache, provided the data hasn't changed significantly since the last execution. AI
IMPACT Highlights that efficient AI agent deployment hinges on managing task repetition and context re-sending, rather than solely on model pricing.
RANK_REASON Article discusses cost optimization strategies for AI agents, not a new release or product.
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