Two out of three American workers are using AI tools their employers don’t know about. That’s not a projection – it’s the finding from Okta’s AI Agents at Work 2026 survey, which sampled 292 executives and 492 knowledge workers across seven countries. The US leads every nation surveyed in shadow AI usage. And while 90% of executives say they’re confident in their visibility into AI tools, more than half their workforce is operating outside official oversight.
On October 1, San Jose-based Classie AI moved to close that gap. The company launched Classie Supervise, the first platform specifically dedicated to real-time enterprise AI agent oversight. It’s the third piece in a platform lineage that started with Classie Discover (April 2026) for visibility and Classie Analyze (July 2026) for context. Supervise adds the intervention layer – the ability to see what agents are doing and stop them when they cross a line.
Here’s how it works. Classie Supervise monitors both sanctioned and unsanctioned AI agents across browsers, endpoints, and enterprise compute environments. Instead of just logging identity – who the agent is – it builds a runtime transcript that captures the full picture: who initiated the activity, what the agent did, what data it accessed, what it cost, and the complete chain of custody. The system integrates the Open Policy Agent (OPA) policy engine, which lets administrators define and enforce operating rules inline. When an agent moves outside approved boundaries, the platform can intervene in real time.
The demand signal is hard to miss. Between April and September 2026, $435 million poured into agent security infrastructure across 12 funding rounds, nine of them agent-focused. The top recipients – Alice and Zenity – accounted for $265 million combined. Meanwhile, 58% of executives told Okta their organization experienced an AI-related security incident or close call in the past year. Classie is betting that urgency translates to buyers: the company promises a five-day deployment timeline, with observability pricing starting at $99 per user.
That pricing figure deserves a caveat. It comes from third-party press coverage – TechStrong and VMBlog – not from Classie’s own announcement. The company directs pricing inquiries to letstalk@classie.ai, which suggests the figure may be a starting point rather than a firm commitment. Tier details, minimums, and whether intervention features cost extra remain unconfirmed.
The broader market context also warrants caution. Cisco’s research from March 2026 – which is vendor-commissioned – found that while 85% of organizations are experimenting with agents, only 5% have reached production. Gartner projects that more than 40% of agentic AI projects will be canceled by the end of 2027. Classie has not published production performance results or measured cost savings from real deployments. The architecture is sound on paper, but the gap between a successful demo and a reliable business process remains wide.
For the average worker, the shift looks like this: instead of choosing between an unapproved tool that gets the job done and an approved one that doesn’t, employees may find themselves working inside a guardrailed environment where their AI assistants are monitored in real time. You configure the boundaries; the system enforces them. The agent becomes an extension of your work, but with a digital paper trail that satisfies the security team. The question is whether that oversight feels like protection or surveillance – and the answer probably depends on how tightly the policies are drawn.
This fits a pattern we’ve been tracking. The execution-layer gateway concept – covered in Post 131169 – highlighted the security perimeter shift to what agents actually do, not just what they say. Oracle’s Fusion Claw (Post 131223) brought native agent orchestration into the ERP stack with its own governance framework. Classie positions at the same observability layer but from the opposite direction: instead of building agents into enterprise software, it watches every agent already running in the environment – sanctioned or not. Whether that outside-in approach can scale alongside the platforms embedding agents natively is the open question.