Raycaster/ Eval

APEX-Agents

gpt-5.4-nano on World 128_RG_03

6/6Pass
Domain
Management Consulting
Category
AI Agents for Cross-Border Regulatory Review
Harness
dual

Grader rubric

Criteria verdict

  1. States that the total 2025 profit for Canada is $0.083B

  2. States that the total 2025 profit for Germany is $0.160B

  3. States that the total 2025 profit for Japan is $0.089B

  4. States that the total 2025 profit for South Korea is $0.207B

  5. States that the total 2025 profit for the UK is $0.076B

  6. States that the total 2025 profit for the US is $0.132B

Prompt excerpt

Task context

Can you use the AmensaMech operational data file to provide the total profit for 2025 for each country listed in it? Assume that revenue for each sector-country pair is calculated by multiplying the Total revenue, the revenue weight, the Sector Margin % for the corresponding sector, and the Country correction factor for the corresponding country. Please refer to the Additional information file for the Sector margin and country correction factor. Assume the sector margin and country correction factor will remain constant until 2030. Provide the answers directly here. Round all the final calculations to 3 decimal places, i.e., $0.001B.

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.