Verified65% confidenceFactExact time
In agentic scenarios, an Agent often initiates multiple rounds of model calls within a single user request, with each round carrying an increasingly longer context, causing costs to grow nonlinearly with conversation depth.
3
Sources
65%
Confidence
Long-term
Relevance
10/8/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedAgent-type tasks use the ReAct think-act-observe loop, and a single user task may trigger dozens or even hundreds of model calls, causing computational overhead to grow non-linearly.79% similarUnverifiedIn Agent workflows, each file the model reads fills the context window further, and every subsequent inference must carry an ever-growing context, causing costs to grow nonlinearly.78% similarUnverifiedIn Agent scenarios, each tool call requires the full context to be re-input into the model, causing token consumption to grow exponentially.77% similarVerifiedEach Agent run incurs actual API call costs, and as the scale of evaluation use cases grows and retries accumulate, costs can mount rapidly.77% similar
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