Raycaster/ Eval

APEX-Agents

gpt-5.4-mini on World131_acd_task09

3/3Pass
Domain
Management Consulting
Category
AI Agents for Infrastructure Finance
Harness
dual

Grader rubric

Criteria verdict

  1. States the R Squared for GE is 0.05

  2. States the R Squared for Hitachi is 0.43

  3. States the R Squared for ABB is 0.27

Prompt excerpt

Task context

EuroGrid wants to understand whether the root cause of its asset failures can be explained by age, load, and/or frequency of weather events. Identify the 3 manufacturers with the highest total failures over the past 5 years across all asset types and then run a multivariate regression on SAIDI for each manufacturer using the asset registry and the extreme weather dataset (filtering out sensors, breakers, and substations, as these assets' failure patterns and/or shorter operational lifespans would skew the regression results). Use the attached file to map countries and regions between the Asset Registry and the weather dataset. For each manufacturer, tell me the R Square of the regression. Round all final answers to 2 decimals. Return your answer directly in here

Response trace

Agent response, tools, files, and edits

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Why Eval exists · why Workspace exists

Public evidence and cloud agents are the same harness.

Eval exists so scores are inspectable—tasks, trajectories, artifacts, and rubric verdicts anyone can open.Workspace exists so people can automate real file work with that harness, and so Raycaster never evaluates work it cannot perform.