2026-10-11 17:11 UTC

Independent evaluations will determine whether Orca-Bench accurately shows current language-model agents can perform realistic on-call diagnosis, remediation, and operational coordination tasks.

state: expiredheat: lowuncertainty: highnovelscott: nonecoding-agents agent-benchmarks oncall-automation

What is this?

Orca-Bench is presented as a benchmark for testing whether language-model agents can handle realistic on-call work, including diagnosis, remediation, and operational coordination. The supplied results establish a broader movement toward real-world agent evaluations: METR operationalized autonomous tasks, while Terminal-Bench tests difficult command-line tasks and reports frontier systems scoring below 50%. However, the snippets do not identify Orca-Bench’s creators, methodology, models evaluated, or results, so its accuracy and independent validation remain unestablished here.

Why it matters to Scott

No intersection found: the supplied material does not connect Orca-Bench or its validation claims to Scott’s documented positions, projects, or any story already tracked by the radar.
queries asked of Scott's wikis
  • realistic evaluations for coding-agent harnesses
  • LLM agents for incident diagnosis and remediation
  • autonomous on-call operations and human escalation
  • agent benchmark validity versus production performance
  • tool-use reliability in long-horizon operational tasks
  • verification and rollback for agent-executed infrastructure changes

Measured heat

no measured readings yet — the hourly heat pass fills this in

How the heat travelled

no chain yet — the hourly chain pass fills this in

Evidence (2) — ⭐ canonical anchor

sourceobjectauthorscorecomments
🟧 hnOrca-Bench: How Ready Are Language Model Agents for Oncall?yruzin185
🟧 echo.paper ⭐Orca-Bench evaluates how ready language-model agents are for realistic on-call work.——

Interpretation history

Decision trace