New DoTime Benchmark Measures How Well AI Agents Manage Real-World Tasks

As AI agents move from chatbots to autonomous assistants, the gap between benchmark performance and real-world usefulness grows wider. Static coding challenges and synthetic Q&A datasets test raw capability, but they don't measure what developers care about: whether an agent can reliably manage a messy, deadline-driven workflow. A new benchmark called DoTime, described in a recent preprint on arXiv, aims to close that gap.

DoTime (Domain-centric Time-sensitive Benchmark) evaluates AI agents on tasks that require planning, prioritization, and adaptation—think scheduling meetings, coordinating project handoffs, or responding to urgent interruptions. Unlike single-turn benchmarks, DoTime presents agents with a continuous stream of events and requires them to make decisions under time pressure. The tasks are grounded in realistic domains such as software development, operations, and personal productivity, making them directly relevant to practitioners building agentic tools.

Preliminary results from the paper are sobering. Even state-of-the-art models struggle with the benchmark's long-horizon tasks, often failing to maintain context, prioritize correctly, or adjust plans when new information arrives. The researchers note that current agents are particularly weak at handling task dependencies and re-prioritizing when deadlines shift—skills that are table stakes for any real deployment.

For developers building AI-powered workflow tools, DoTime offers both a reality check and a roadmap. It highlights where current agents fall short—not just in knowledge but in executive function—and provides a concrete way to track progress. As agentic products move toward production, benchmarks like this become essential for evaluating whether a model can actually be trusted with a to-do list.

The full paper, including the benchmark suite and evaluation harness, is available on arXiv. The authors plan to release the dataset and scoring code to encourage broader adoption, which could help standardize how the industry measures agent reliability in time-sensitive contexts.