Agent success now depends less on completing tasks than on whether every counterparty with standing to reject recognizes the action as authorized.

Drouin's projects trace how agent evaluation evolved from checking task completion to verifying that outcomes are grounded, cited, and structurally real. The progression reveals a field learning that the hardest problem is not capability but verifiability.

Durable agent adoption depends less on AI capability than on whether a domain already has organizational infrastructure for humans to contest, evaluate, and reverse the output before the cost of errors falls on the wrong people.