Compare AI ecosystems at the same permission level

Ahmad Turki Obaidat’s “Let an AI Agent Earn Its Next Permission” proposes increasing an agent’s capabilities in stages, while Yasmin Muhamad Hamad’s “GPT, Claude or Gemini: the $10M bet is bigger than the model” argues for comparing ecosystems on the same real-world tasks and beyond output quality.

A useful way to combine these ideas is to keep the task and permission boundary fixed in each comparison. For one fictional workflow, test every system first in read-only mode, then with a human approval step, and only then—if appropriate—with a narrow, agreed action. At each stage, record task completion, errors, recovery, and whether the system stayed within its permission. Otherwise, a stronger result might reflect broader access rather than a better fit.

Before moving to the next stage, the agreed decision owner can explain the trade-off and consult the people affected. What failure would make you stop a comparison before granting the next permission?