SentinelBench: A Benchmark for Long-Running Monitoring Agents
AI agents traditionally rely on continuous action, but long-running tasks benefit from sustained attention. SentinelBench is a new benchmark with 100 tasks across 10 synthetic web environments to evaluate monitoring agents based on task completion, reaction time, and resource use. Initial results show clear distinctions in agent behavior.
[2606.05342] SentinelBench: A Benchmark for Long-Running Monitoring Agents
[Submitted on 3 Jun 2026]
Title:SentinelBench: A Benchmark for Long-Running Monitoring Agents
View a PDF of the paper titled SentinelBench: A Benchmark for Long-Running Monitoring Agents, by Matheus Kunzler Maldaner and 7 other authors
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Abstract:AI agents are increasingly asked to carry out work that spans minutes, hours, or longer. Yet the default model of agent behavior is continuous action: issuing tool calls, refreshing pages, searching for alternatives, or otherwise trying to force progress. This is the wrong approach for many long-running tasks, which are better served by a strategy of sustained attention. Instead, agents should monitor an environment, notice when an external event makes progress possible, then respond promptly without wasting resources while waiting. To measure progress on this class of tasks, we introduce SentinelBench, an open-source benchmark for time-evolving monitoring tasks.
SentinelBench contains 100 tasks across 10 synthetic web environments, including email, calendars, finance, professional networking, and entertainment. Each environment exposes a live web interface and replays a scripted sequence of events, requiring agents to navigate and reason about web pages whose state shifts underfoot. SentinelBench measures task completion, reaction time, and resource use, exposing the tradeoff between responsiveness and cost. We report results across three models and two browser-agent harnesses, establishing performance baselines for future comparison and demonstrating how agent design choices can dramatically impact key metrics. Together, these results show that SentinelBench distinguishes meaningful differences in agent behavior.
Comments: 18 pages, 16 figures
Subjects:
Artificial Intelligence (cs.AI)
ACM classes: I.2
Cite as: arXiv:2606.05342 [cs.AI]
(or arXiv:2606.05342v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.05342
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Amanda Swearngin [view email] [v1] Wed, 3 Jun 2026 18:32:00 UTC (13,778 KB)
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