Enterprise audit teams are finding that the evidence trail they depend on does not survive contact with AI agents. Financial and operational data still sits across NetSuite, human resources platforms and data lakes, but the judgment calls once recorded in email threads, Slack messages and tick marks are increasingly made by software moving faster than any reviewer can follow.
That shift lands hardest in regulated industries, where AI governance has become a board-level question rather than a compliance checkbox — the theme running through Workiva Inc.’s Amplify conference. Agents deliver real return by absorbing repetitive work, yet the risk they introduce sits in what auditors can no longer see, according to Josh Robinson (pictured), chief audit executive at Vast Space LLC.
“With humans, we could understand they were using their judgment, they were exercising probability, but we could see it,” he said. “There was either handwritten notes or there were tick marks or there was email chain or Slack messages. That has all been removed from the equation. So, for me, the risk is in the unknown.”
Governance, NetSuite and the audit trail behind AI agents
The answer is not new tooling so much as old discipline. Internal audit has spent years pressing the concepts of completeness and accuracy, and those tests can be applied step by step across a multistep agent workflow, Robinson explained.
“If you can demonstrate completeness and accuracy as an auditor, generally that allows you to reach conclusions about the assurance of datasets or audit tests you’re doing,” he said.
Accountability is the harder question. At Vast Space, which builds commercial space stations and carries astronaut safety risk alongside regulatory exposure, control gaps have consequences well beyond a restatement. Ownership stays with the person signing the work, regardless of which tool produced it.
“As an audit leader, if it comes to my desk and I’m going to put my signature on it, it pretty darn well better be the right answer,” Robinson said.
Efficiency depends on getting data out of silos first. Workiva’s specialized AI agents and Knowledge intelligence layer target that gap, and Robinson, a customer since 2017, said the appeal is one connective layer spanning NetSuite, HR systems, data lakes and whichever model a company has chosen.
“I can go to my CFO and be like, OK, we use the likes of a NetSuite and an HRIS and this and this, and this is our data lake,” he said. “And we’re using Claude, but now look at Workiva who can connect to those 5 different things, bring it in and create agents simply that is going to solve X, Y, Z problem for you.”
