Trust in agentic AI, however, is improving. OutSystems reports that 73% of respondents express either high or moderate trust in letting agents to act autonomously, a rise of around 10% compared to a similar survey the company undertook last year. Trust in code or workflows generated by third-party AI tools is slightly lower, at 67%, a substantial increase from the prior year’s figure, when only 40% ‘mostly trusted’ generative AI to write code without human help.
Only 36% of respondents say they have a centralised approach to AI governance, while 64% say they lack such a facility, and 41% rely on rules implemented on a per-project basis. Two-thirds say building human-in-the-loop checkpoints is technically difficult because it requires orchestration that can pause agents – in effect inserting manual braking on operations that might be fully autonomous.
Many organisations appear to be deploying looser oversight models, although it is not clear if that is a result of greater trust in models or whether business functions are under pressure to deploy AI regardless of security or reliability concerns. If the trend to loosen oversight continues, the report’s authors note that agentic AI adoption may advance faster than the methods of accountability that many consider important.
Firms that want to scale agents in regulated or mission-critical settings should treat orchestration and auditability as part of the product, the survey’s findings state. When compliance checks consider a business’s operations, breadcrumb trails in the form of logfiles and defined responsibilities are considered important elements of any agentic AI rollout.
The report says 94% of leaders are concerned about “AI sprawl”, which is not defined, but could be inferred to be a lack of a centralised management platform that oversees all AI deployments in the enterprise. 39% are very or extremely concerned about the issue, and only 12% currently use a centralised platform to keep that sprawl under control.
The full survey can be accessed here.
