aiqbench analysis
SRE-Marathon introduces a critical benchmark for autonomous agents by shifting from isolated incident response to continuous, long-horizon operations within live Kubernetes environments. The study reveals that current methods struggle significantly with overlapping faults originating from code changes, achieving less than 42% success in completing repairs while issues remain active. This work highlights the substantial gap between episodic testing and the complex reality of production reliability engineering for AI systems.
SRE-Marathon: A Continuous, Change-Driven Benchmark for Autonomous Site Reliability Agents
arXiv:2609.33023v1 Announce Type: new Abstract: Benchmarks for site reliability engineering (SRE) agents are typically episodic: one fault is injected, the agent receives an incident task, and its response is scored. Production operation is not. Incidents surface through noisy alerts, overlap in time, and often originate from code or configuration changes. We present SRE-Marathon, a benchmark for long-horizon, continuous SRE operation. An agent is invoked at a fixed cadence with cumulative alert history and a persistent workspace while operating a live two-zone Kubernetes deployment as a fault orchestrator injects overlapping faults according to a seeded, production-calibrated schedule. Curated code and configuration changes deployed through the same build pipeline available for repair. Each run is recorded into a sealed bundle and scored offline: Marathon-Score credits each injected fault for ordered progress through correlation, localization, and repair, with all metrics computed deterministically from recorded system evidence. Across three applications and about sixty faults per run, the best of 10 methods reaches only 41.3 out of 100. Agents often correlate and localize faults, but almost never complete repairs while the faults remain active.
Abstract:Benchmarks for site reliability engineering (SRE) agents are typically episodic: one fault is injected, the agent receives an incident task, and its response is scored. Production operation is not. Incidents surface through noisy alerts, overlap in time, and often originate from code or configuration changes. We present SRE-Marathon, a benchmark for long-horizon, continuous SRE operation. An agent is invoked at a fixed cadence with cumulative alert history and a persistent workspace while operating a live two-zone Kubernetes deployment as a fault orchestrator injects overlapping faults according to a seeded, production-calibrated schedule. Curated code and configuration changes deployed through the same build pipeline available for repair. Each run is recorded into a sealed bundle and scored offline: Marathon-Score credits each injected fault for ordered progress through correlation, localization, and repair, with all metrics computed deterministically from recorded system evidence. Across three applications and about sixty faults per run, the best of 10 methods reaches only 41.3 out of 100. Agents often correlate and localize faults, but almost never complete repairs while the faults remain active.
Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
arXiv:2609.33023 [cs.AI]
(or arXiv:2609.33023v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.33023 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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