APEX-Agents · Management Consulting
World131_acd_task09
APEX-Agents task World131_acd_task09 in AI Agents for Infrastructure Finance. Compare dual-harness agent runs across models, scores, and public traces.
Task prompt
What the agent was asked to do
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
Published trajectories
Agent runs on this task
Curated dual-harness runs (parsed + original sandbox). Best scored run per model.
| Model | Harness | Score | Result | Links |
|---|---|---|---|---|
| Gemini 3.1 Pro | dual | 3/3 | Pass | Run detailsPublic trace |
| GPT-5.4 | dual | 3/3 | Pass | Run detailsPublic trace |
| GPT-5.4 mini | dual | 3/3 | Pass | Run detailsPublic trace |
| GPT-5.4 nano | dual | 3/3 | Pass | Run detailsPublic trace |
| GPT-5.5 | dual | 0/3 | Fail | Run detailsPublic trace |
Grading rubric
Rubric criteria
Runs are graded against these criteria. Open a run for model-specific verdicts.
States the R Squared for GE is 0.05
States the R Squared for Hitachi is 0.43
States the R Squared for ABB is 0.27