The author discovered that 56% of their AI agent's work was unaccounted for due to mislabeling sessions. A single session, titled "Quick check-in," accounted for 26.6% of all messages but was not categorized as output or failure. Further analysis revealed that the "ship work" category was significantly underestimated, with the agent actually spending over a third of its time writing posts. The residual category also contained other unclassified sessions, including some with raw timestamps and one titled "Greeting," indicating broader issues with session tracking and labeling. AI
IMPACT Highlights potential issues in tracking and attributing AI agent activity, suggesting a need for more robust logging and classification mechanisms.
RANK_REASON The item is a personal reflection on AI agent usage and data analysis, not a release or significant industry event.
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