Companies are deploying AI agents that book meetings, spend budget, write code and talk to customers, but most of those agents run on a shared API key with no distinct identity, no budget ceiling, and no audit trail, which is untenable the moment something goes wrong or a regulator asks a question. This product gives every AI agent its own identity, provisioned like an employee: authentication, scoped permissions, per-agent spend limits with configurable reset periods, and a tamper-resistant log of every action the agent took, from tool calls to LLM requests to the final output.
The customer is the enterprise platform or security team standing up AI agents at scale, who needs to answer basic governance questions, which agent did this, what was it allowed to spend, what tools could it touch, and can we prove it, and currently cannot. Instead of building this in-house or bolting it on after an incident, they adopt a control plane that sits between their agents and the systems those agents touch.
The wedge is being infrastructure, not a feature: a dedicated identity and audit layer that integrates with whatever agent framework or LLM provider a company already uses, rather than a governance module tacked onto one vendor's agent platform. That positions it to become the default layer enterprises route every agent through, the way identity providers became the default layer for human employees.