Unverified50% confidenceDecision RuleExact time
In agent workflows, if the token consumption of a task suddenly reaches 3 times that of adjacent tasks, it is usually not because the task is hard but because the task was split badly, with a vague specification causing the agent to repeatedly trial and error; high-scoring engineers treat cost anomalies as an alarm for specification problems.
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Long-term
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10/21/2026
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Related Claims
UnverifiedTask granularity refers to the size of the subtasks split off for the agent: the finer the granularity, the easier each step's output is to verify and the more precise the error localization, but splitting overhead and the risk of losing context rise; too coarse a granularity makes verification difficult and rework costly.77% similarUnverifiedPolling at a fixed interval has two problems: when there are many tasks it causes a large number of invalid requests that consume quota and may even trigger rate limiting; when tasks take a long time it may lengthen the total waiting time73% similarUnverifiedThe test's limitations include: the three tasks are a small sample and skew toward front-end coding tasks, not involving large refactors or multi-day migrations; token counts depend heavily on how each tool organizes its agent loop, and a different mix of tasks could change the ratio.73% similarUnverifiedSetting Maximum Iteration too high may waste cost (tokens), while setting it too low may truncate the task before completion; it is a key parameter that requires trade-offs in Agent tuning.73% similarVerifiedThe AI agent field faces a 'compounding error problem,' where as the number of task steps increases, small errors at each step accumulate and amplify, ultimately causing the entire task to deviate from expectations.73% similar
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