SentinelBench: A Benchmark for Long-Running Monitoring Agents

2026-05-27 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, extended

Summary

SentinelBench is an open-source benchmark designed to evaluate AI agents on long-running, time-evolving monitoring tasks, addressing the inefficiency of continuous action in such scenarios. It comprises 100 tasks across 10 synthetic web environments, including email, calendars, and finance, each featuring a live web interface with scripted event sequences. The benchmark measures task completion, reaction time, and resource utilization, highlighting the trade-off between responsiveness and cost. Initial evaluations using GPT-5.4 (low reasoning), Qwen 3.5:9b, and GPT-4o with two agent harnesses (a "sleep(time)" tool versus a "wait_for(condition, timeout)" tool) demonstrate that the "wait_for" tool dramatically reduces costs, with GPT-5.4's median task cost being 5.1x lower, and up to 9.7x lower for 40-minute tasks, while maintaining or improving success rates.

Key takeaway

For AI Engineers designing or deploying agents for long-running monitoring tasks, you should prioritize architectures that incorporate explicit waiting mechanisms. Implementing tools like "wait_for(condition, timeout)" can drastically reduce your operational costs and improve efficiency, especially as task durations increase. Relying on continuous polling or simple "sleep" commands will lead to significantly higher token usage and potential task failures.

Key insights

For long-running tasks, AI agents must adopt sustained attention strategies, monitoring for external events to optimize resource use and reaction time.

Principles

• Continuous agent action is inefficient for monitoring.
• Agents must wait for external environment changes.
• Tool design critically impacts agent cost and performance.

Method

The "wait_for(condition, timeout)" tool captures page snapshots, computes diffs, and uses an LLM to evaluate conditions on new changes, with periodic reloads and rate limits.

In practice

• Integrate "wait_for" tools into agent harnesses.
• Benchmark agents on reaction time and token cost.
• Vary task criteria (absolute vs. relative).

Topics

• SentinelBench
• AI Agents
• Monitoring Tasks
• Web Environments
• Resource Optimization
• Agent Benchmarking
• Tool Use

Code references

• microsoft/sentinel_environments

Best for: AI Architect, Research Scientist, CTO, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.