AI Agents Are Deciding—Do You Trust The Output?

Nalini Garg, Associate Vice President, Deloitte.

​I’ve sat in enough enterprise AI reviews at this point to notice a pattern: Leadership teams describe themselves as confident in their AI output, right up until someone asks who actually checked it. That gap between stated confidence and real verification is the problem I keep running into, and it’s rarely about the model. It’s about what happens, or doesn’t happen, around it.

The data backs up what I’m seeing in the field. A 2026 Workiva survey found that 84% of executives are confident in AI-generated content reaching critical reports without human review, while only 11% believe their underlying data quality can actually support that confidence. Grant Thornton found something similar: Seventy-eight percent of leaders lack confidence they could pass an independent AI governance audit within 90 days. Confidence, in other words, is outrunning verification almost everywhere I look.

In my own work advising enterprise leaders on AI adoption, I’ve found that when someone tells me they “don’t trust” an AI output, they’re almost never describing one problem. They’re describing one of three, and conflating them is exactly why so many governance efforts stall out.

The first is a data problem: The model was fed something wrong, incomplete or stale, and no amount of polish in the output changes that. It’s the easiest to spot but rarely the whole story.

What actually closes this gap, in my experience, isn’t a policy memo. It’s what I think of as an agent harness: the engineering layer that decides what tools an AI system is allowed to call, what data it can touch, when it has to stop and ask a person before proceeding and what trail it leaves behind. I tell clients to think of it less as a guardrail and more as a receipt requirement. A model gives you an answer; a harness makes it prove how it got there before anyone downstream is asked to act on it.