SentinelBench: A Benchmark for Long-Running Monitoring Agents

Researchers introduce SentinelBench, an open-source benchmark designed to evaluate AI agents performing long-running monitoring tasks across 10 synthetic web environments. The benchmark addresses a critical gap in agent evaluation by measuring task completion, reaction time, and resource efficiency—metrics that reveal how well agents balance responsiveness with cost-effectiveness in time-evolving scenarios.

SentinelBench represents a meaningful contribution to AI agent evaluation infrastructure, tackling a problem that existing benchmarks largely overlook. Most current agent assessments focus on single-turn or rapid-completion tasks, but real-world applications frequently require sustained monitoring—email management, financial tracking, deadline alerts, and content discovery. The benchmark's design acknowledges that continuous action is often wasteful; effective agents should maintain attention while remaining dormant until environmental changes warrant intervention.

The research emerges from broader industry recognition that AI agents need sophistication beyond large language models. As organizations deploy agents for business operations, evaluation frameworks must capture nuanced behavioral patterns. SentinelBench's inclusion of 100 tasks across finance, professional networking, and entertainment domains reflects practical use cases where resource constraints matter. The benchmark tests three models against two different browser-agent architectures, providing multiple data points that reveal how design decisions—harness implementation, model selection, monitoring strategies—directly influence performance metrics.

For developers building agent systems, SentinelBench offers concrete performance baselines and methodology for optimization. The emphasis on resource efficiency addresses enterprise concerns about API costs and computational overhead, particularly relevant as agent deployment scales. The benchmark's time-evolving nature also introduces temporal complexity absent from static evaluation frameworks, better reflecting production environments where task state changes asynchronously.

Future development likely involves expanding task diversity, increasing benchmark difficulty, and potentially integrating real web services beyond synthetic environments. The open-source release enables community contribution, establishing shared evaluation standards across the industry.

• →SentinelBench introduces the first dedicated benchmark for evaluating AI agents on long-running monitoring tasks requiring sustained attention rather than continuous action.
• →The benchmark measures task completion, reaction time, and resource use simultaneously, exposing fundamental tradeoffs between responsiveness and operational cost.
• →Results across three models and two browser-agent harnesses establish baseline performance metrics and demonstrate how architecture choices dramatically impact agent behavior.
• →The 100 tasks span practical domains including email, calendars, finance, and networking, reflecting real-world monitoring scenarios agents encounter.
• →Open-source availability enables standardized evaluation across the agent development community, establishing shared performance comparison standards.