Every agent conversation I’m in lately lands on the same question: which platform can we trust to run our agents?
It’s the wrong question, and it’s worth catching before it sets your whole architecture.
Trust is a chain. The cloud provider, the vendor, the model underneath. That chain held up fine when software did exactly what you told it. But an autonomous agent retrieves data, invokes tools, and takes an action across three systems you don’t own. Go looking for what actually happened, and there’s often no record you can independently check. You’re trusting the vendor’s word about the vendor’s own product. Right?
So approach it this way instead. For every consequential action… approving a payment, touching regulated data… make the agent leave evidence. Who asked, what it read, which tools it called, what it decided. Signed and time-stamped, so you can reconstruct it later without taking anyone’s word for it.
And no, this isn’t some new discipline we invented for AI. It’s audit trails, provenance, non-repudiation. The same controls we’ve run for twenty years, applied at machine speed.
Trust your vendor… that’s fine. Just don’t confuse trust with proof. Proof is what you can hand an auditor when the agent gets it wrong.
Source: Agents Expose the Limitations of Trust
Originally posted on LinkedIn.
RationalAI helps organizations turn ideas like this into results: AI strategy and advisory services. Previous article: Make the Agent Earn Trust (Read-Only First)
