When hiring for AI agents with wallet access, what matters more: model quality or policy-layer design?

I keep seeing teams talk about AI agents that can hold wallets, trigger transactions, rebalance funds, or take onchain actions with very little human input. The demos look sharp, but from a hiring point of view, I am not sure what we are actually supposed to trust.

If a company is hiring engineers for AI x Web3 systems where an agent can touch money, is strong model knowledge enough? Or does trust come more from how the person designs policy layers around the model: permission boundaries, transaction limits, approval paths, signer separation, monitoring, rollback logic, and human override?

What worries me is that a lot of people can speak well about prompts, agent loops, and autonomous workflows, but that does not automatically mean they understand financial risk, smart contract exposure, or failure containment. In this kind of role, I would trust someone who thinks clearly about policy enforcement and system boundaries more than someone who only talks about model performance.

For teams hiring in this space, what proof signals actually matter most when evaluating candidates building AI agents with wallet access?