[2607.28545] ORCA-bench: How Ready Are Language Model Agents for Oncall?
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arXiv:2607.28545 (cs)
[Submitted on 30 Jul 2026]
Title:ORCA-bench: How Ready Are Language Model Agents for Oncall?
Authors:Albert Gong, Kyuseong Choi, Abhineet Agarwal, Jason Schechner, Ryan Huang, Raj Agrawal, Anish Agarwal, Raaz Dwivedi<br>View a PDF of the paper titled ORCA-bench: How Ready Are Language Model Agents for Oncall?, by Albert Gong and 7 other authors
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Abstract:Large language models can write, patch, and search code, but oncall root cause analysis (RCA) demands something different: reasoning over noisy metrics, logs, traces, and source code, starting from ambiguous user-facing reports, often hours after the incident began. We introduce ORCA-bench, a benchmark that puts general-purpose coding agents in a production-fidelity oncall setting. ORCA-bench pairs a live OpenTelemetry-instrumented microservice system--exposing six days of metrics, logs, and traces through real telemetry interfaces (Prometheus, Jaeger, and OpenSearch via Grafana) and full source-code access--with 1,079 RCA tasks that systematically vary report specificity, time-to-detection, and co-occurring fault scenarios. Ground-truth symptoms are curated and signed off by expert SREs, and our LLM-as-judge is independently re-scored by humans (Cohen's $\kappa_w=0.90$). Across five frontier agents, the best RCA Accuracy is 25.3% on Medium-difficulty tasks (the realistic-input setting) and 10.0% on Hard--a gap that remains even with Claude Fable 5. The weakest model hallucinates an implausible root cause in 40% of incident reports, and removing source-code access degrades every metric. Crucially, these are performances on a curated 50 GB / six-day testbed with tasks investigated in isolation on a system whose code and instrumentation are public. Since real production systems are order of magnitudes larger, more dynamic, and more idiosyncratic, the gap we report is a lower bound on the engineering investment required before frontier coding agents can be safely entrusted with production reliability. We release the public set at this https URL.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as:<br>arXiv:2607.28545 [cs.CL]
(or<br>arXiv:2607.28545v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.28545
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arXiv-issued DOI via DataCite (pending registration)
Submission history<br>From: Albert Gong [view email]<br>[v1]<br>Thu, 30 Jul 2026 17:14:07 UTC (220 KB)
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