APEX-Agents · Management Consulting

World 129_CY_Task 6

8/8Pass

APEX-Agents task World 129_CY_Task 6 in AI Agents for Precedent Transaction Analysis. Compare dual-harness agent runs across models — rubric criteria, scores, and public traces.

AI Agents for Precedent Transaction AnalysisManagement Consulting World 129Dual harnessGrader: rubric

Task IDtask_d8d2c6bd61d548cba10f59af2d6c9559

WorldManagement Consulting World 129

Expected outputmessage_in_console

Published runs5 models · dual config

Task prompt

What the agent was asked to do

Using Brightpath's Discount Approval Logs, review each approver’s total score and rank. Reply to me with a short message here, outlining your findings. Scores are determined using four criteria: 1. Violated Policy Threshold: Score 1 goes to the approver with the most deals exceeding the policy threshold; score 4 goes to the fewest. Scores 2–3 follow their ranking. 2. Negotiation-Based Discounts: Score 1 for approving the most negotiation-driven deals exceeding the threshold; score 4 for the fewest. Scores 2–3 follow. 3. Pilot-Program Discounts: Same logic as in #1, scoring based on deals exceeding the threshold due to pilot-program discounts. 4. Level of Approval: Score 1 for approving the fewest CFO-level deals within policy; score 4 for the most. Scores 2–3 follow. Notes: - Round all scores to the nearest whole number. - Ties receive the same score (e.g., both highest = 1, both fewest = 4, middle = 2). - For ties in total score, use Director-level approval counts from criterion (4) as the tiebreaker.

Published trajectories

Agent runs on this task

Curated dual-harness runs (parsed + original sandbox). Best scored run per model.

Model	Harness	Score	Result	Links
GPT-5.5showcase	dual	8/8	Pass	Share page Public trace
Gemini 3.1 Pro	dual	4/8	Fail	Share page Public trace
GPT-5.4	dual	8/8	Pass	Share page Public trace
GPT-5.4 mini	dual	8/8	Pass	Share page Public trace
GPT-5.4 nano	dual	8/8	Pass	Share page Public trace

Grading rubric

Criteria and grader verdict (showcase run)

States that Daniel Holden's score is 12
Pass
Evidence: TEXT_RESPONSE table row: “1 | Daniel Holden | 12”. Assessment: Criterion “States that Daniel Holden's score is 12” is met; the response clearly lists Daniel Holden with total score 12.
States that Michael Chen's score is 11
Pass
Evidence: TEXT_RESPONSE table row: “2 | Michael Chen | 11”. Assessment: Criterion “States that Michael Chen's score is 11” is met; the response clearly lists Michael Chen with total score 11.
States that Sarah Martinez's score is 11
Pass
Evidence: TEXT_RESPONSE table row: “3 | Sarah Martinez | 11”. Assessment: Criterion “States that Sarah Martinez's score is 11” is met; the response clearly lists Sarah Martinez with total score 11.
States that Aisha Roberts' score is 7
Pass
Evidence: TEXT_RESPONSE table row: “4 | Aisha Roberts | 7”. Assessment: Criterion “States that Aisha Roberts' score is 7” is met; the response clearly lists Aisha Roberts with total score 7.
States that Daniel Holden is ranked first
Pass
Evidence: TEXT_RESPONSE table row: “Rank 1 | Daniel Holden | Total score 12”. Assessment: Criterion “States that Daniel Holden is ranked first” is met; Daniel is shown at rank 1.
States that Michael Chen is ranked second
Pass
Evidence: TEXT_RESPONSE table row: “Rank 2 | Michael Chen | Total score 11”. Assessment: Criterion “States that Michael Chen is ranked second” is met; Michael is shown at rank 2.
States that Sarah Martinez is ranked third
Pass
Evidence: TEXT_RESPONSE table row: “Rank 3 | Sarah Martinez | Total score 11”. Assessment: Criterion “States that Sarah Martinez is ranked third” is met; Sarah is shown at rank 3.
States that Aisha Roberts is ranked fourth
Pass
Evidence: TEXT_RESPONSE table row: “Rank 4 | Aisha Roberts | Total score 7”. Assessment: Criterion “States that Aisha Roberts is ranked fourth” is met; Aisha is shown at rank 4.