| Product | Classie Supervise |
| Website | classie.ai |
| Category | Enterprise Security / AI Governance |
| Pricing | From $99/user (observability tier) |
| Deployment | 15 minutes to install, 5 days to monitored posture |
| Enforcement | Open Policy Agent (OPA) rules |
| GA Date | October 1, 2026 |
What Is Classie Supervise?
Classie Supervise gives CIOs and CISOs a unified view of all AI agent activity across their enterprise. It continuously discovers both sanctioned and unsanctioned AI agents operating on endpoints, in browsers, and across enterprise compute environments, then lets you monitor what they do and enforce rules about what they’re allowed to do.
The product is the third module in Classie’s platform, following Discover (April 2026) and Analyze (July 2026). Supervise adds real-time enforcement: you write rules in Open Policy Agent (OPA) format, and Classie applies them as agents execute, intervening when an agent moves outside approved boundaries.
Key Features
Discover Sanctioned and Shadow AI
Classie automatically finds AI tools, agents, and models running in your environment — both the ones IT approved and the ones employees installed themselves. For each agent, it maps the people using it and the data it can access. This is the “shadow IT” problem applied to AI agents, and Classie surfaces it within days of deployment.
Runtime Transcripts
As an AI agent operates, Classie creates a transcript that records the agent’s identity, the user who triggered it, the context of the interaction, the data accessed, the intent inferred, and the actions taken. These transcripts are searchable and reviewable — the equivalent of an audit log for AI operations.
OPA-Based Enforcement
Classie uses Open Policy Agent rules and lightweight inline sensors to enforce controls in real time. If an agent tries to access data outside its approved scope or take an action that violates policy, Classie blocks it. The rules are written in OPA’s Rego language, which security teams may already know from Kubernetes and infrastructure policies.
15-Minute Installation
The platform deploys through lightweight sensors on endpoints. Classie claims installation takes 15 minutes and begins discovery immediately, with a fully monitored enterprise posture within five days. The sensors are designed to have minimal performance impact.
Who Is This For?
- CISOs and security teams who need visibility into AI agent activity across the enterprise
- Compliance officers required to demonstrate governance over AI systems
- IT teams dealing with shadow AI adoption — employees using unapproved AI tools
- Enterprises in regulated industries (finance, healthcare, legal) where AI governance is mandatory
Pros
- Discovers shadow AI agents automatically
- Runtime transcripts create a full audit trail
- OPA-based rules — familiar to security teams
- 15-minute deployment
- Real-time enforcement, not just monitoring
- Covers endpoints, browsers, and compute environments
Cons
- $99/user minimum — enterprise pricing only
- Requires endpoint agent installation
- OPA rule writing has a learning curve
- New product (GA October 2026)
- Inline enforcement could create false positives
- Coverage depth depends on sensor placement
Verdict
Classie Supervise addresses the growing “AI governance gap” in enterprises. Companies are deploying AI agents faster than security teams can evaluate them, and employees are adopting AI tools without IT approval. Classie brings this under control with discovery, monitoring, and enforcement in a single platform.
The OPA integration is a smart architectural choice. Rather than inventing a proprietary policy language, Classie uses the same engine that enterprises already use for Kubernetes and cloud infrastructure policies. Security teams can apply existing skills and, in some cases, existing rule sets.
The main question is whether $99/user is justified by the value. For a 500-person company, that’s $49,500/year for observability alone. The pitch has to be that the alternative — an AI data breach, a compliance violation, or a shadow agent exfiltrating proprietary data — costs orders of magnitude more. For regulated enterprises, this is an easy argument to make.
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