SentinelBench: A Benchmark for Long-Running Monitoring Agents
Quick Answer
SentinelBench introduces a benchmark for long-running monitoring agents, featuring 100 tasks across 10 web environments.
Quick Take
It measures task completion, reaction time, and resource use, highlighting the tradeoff between responsiveness and cost, with results showing significant performance variations across different agent designs.
Key Points
- SentinelBench includes 100 tasks in environments like email and finance.
- It measures task completion, reaction time, and resource efficiency.
- The benchmark reveals tradeoffs between responsiveness and cost.
- Results indicate significant performance differences among agent designs.
- Three models and two browser-agent harnesses were evaluated.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 05342v1 Announce Type: new 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.
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