APEX-Agents
gpt-5.5 on World126_JD_04
Grader rubric
Criteria verdict
States that the Weighted Avg Expected Annual Return for KO is 6.31
FailStates that the Weighted Avg Expected Annual Return for MDLZ is 6.71
FailStates that the Weighted Stdev Expected Annual Return for KO is 2.59
FailStates that the Weighted Stdev Expected Annual Return for MDLZ is 2.77
Fail
Prompt excerpt
Task context
Please check how KO and MDLZ differ in expected upside once we apply the ESG and GLP-1 filters and account for each investor’s maximum allowable ESG risk level. Use the survey data and the ESG risk thresholds to determine which respondents are eligible to hold each company. Then calculate the confidence weighted average and standard deviation of expected annual return for KO and MDLZ. Show each company’s weighted average and standard deviation of expected annual returns. Round only the final results, going to two decimal places.
Response trace
Agent response, tools, files, and edits
On a phone, the interactive viewer works best full-screen — pick the narrative report or the files & trajectory workspace.
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.