Task Granularity
Refers to the size of the subtasks divided out to an AI agent. The finer the granularity, the easier each single-step output is to verify and the more precise the error localization, but the overhead of division and the risk of context loss increase; if the granularity is too coarse, verification becomes difficult and rework is costly.
Timeline (last 90 days)
Task 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.
A common compromise in practice is "small steps, fast iteration plus checkpoints": each subtask comes with an explicit input/output contract and acceptance conditions, verification is triggered as soon as the agent completes a step, and only after it passes does work move to the next step.
All Facts (2)
Task 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.
50%UnverifiedA common compromise in practice is "small steps, fast iteration plus checkpoints": each subtask comes with an explicit input/output contract and acceptance conditions, verification is triggered as soon as the agent completes a step, and only after it passes does work move to the next step.
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