Can LLM Agents Estimate Their Own Task-Completion Time Horizon?

The increasing reliability of LLM agents has led to soaring demand for persistent assistants and autonomously running agents. Yet an overlooked limitation of current systems is their inability to reliably model how long their own runs will take. Despite its practical importance for agent scheduling, planning, and real-time coordination, this phenomenon has not been systematically studied. To bridge this gap, we propose an evaluation framework that decomposes run-time self-estimation into four categories: pre-run, post-run, relative, and compositional estimation. We further investigate prompting- and training-based calibration methods to reduce estimation error. We find that models appear to inherit general knowledge about task duration from training data, yet they anticipate poorly how much work their own runs will involve and how the deployment that executes them affects run time. These results expose an important weakness in how modern LLMs anticipate their own run time, with practical implications for persistent agents, time-sensitive applications, and real-time human–AI coordination.