A roundup published in 2026 cites a Boomi study, reported as July 2026, in which 86% of enterprises had moved AI agents from pilot to production while only 34% said they trusted the actions those agents take. We have read this through secondary coverage, so treat the exact figures as indicative. The shape of the gap is the useful part.
Why the gap exists
- Deployment is a project; trust is a track record. Agents can go live in weeks, but confidence comes from months of observed behaviour.
- Most teams can see what an agent answered, but not why it acted. Without traces, trust has nothing to stand on.
- Responsibility is vague. When an agent takes a wrong action, many organizations can't say who owns the outcome.
How to close it on your own system
- Log every tool call with inputs, outputs and the evidence used, in a form an auditor can read.
- Start with narrow permissions and widen them only as the override rate falls.
- Publish a one-page statement of what the agent can do, what it cannot, and who owns it.
- Review a sample of actions weekly and track the error rate openly.
Trust is not a feeling to be marketed to staff. It is an operating record you can show them.