AI agents misjudge task duration. Studies show errors of up to 10x
Research on tested language-model agents shows that their time estimates can differ sharply from actual execution. In evaluated settings, errors reached up to about 10 times the real task duration.
Research on tested language-model agents shows that their time estimates can differ sharply from actual execution. In evaluated settings, errors reached up to about 10 times the real task duration.
What do the results show?
In tests of coding assistants Claude Code and Codex, the models systematically overestimated task duration. In the evaluated setting, Codex errors reached up to about 10 times the actual execution time.
Multistep tasks remain difficult
In an independent study of multistep agentic settings, pre-task duration estimates differed from actual execution time by roughly 5 to 10 times.
Timestamps do not solve the problem
Across 76 scenarios, no evaluated agent exceeded 65% normalized alignment with human temporal preferences, even when timestamps were provided.
What does this mean for automation?
In practice, a tested agent's time estimate is better treated as an approximation. Longer autonomous tasks should use external timing, checkpoints and execution limits instead of relying only on the model's own ETA.
Sources: Your Agents Are Not Time Aware, Can LLMs Perceive Time? An Empirical Investigation, Your LLM Agents are Temporally Blind
Source article: the-decoder.com
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