Inline authorization for custom AI agents, with isolated data access and per-action receipts

VisIQ Labs describes itself as a runtime trust layer, and the distinction it draws is about where enforcement happens. Agents built in-house tend to inherit broad credentials and then rely on instructions to stay within bounds, which leaves no reliable record of what was actually attempted. VisIQ moves the decision to execution time: before an agent retrieves a document, calls a tool, or approves an action, the request is evaluated against that agent's assigned trust tier and the business function it exists to serve. Data access is isolated per agent rather than shared, and each permitted or refused action produces a receipt suitable for audit.

The control point is framework-level rather than proxy-level, with governance applied inline for agents written against LangChain, the OpenAI Agents SDK, LlamaIndex, the Vercel AI SDK, Mastra, VoltAgent, Semantic Kernel, and coding agents including Claude Code. Around that enforcement layer the platform adds discovery, to find the agents an organization is already running; agent red teaming, to probe them adversarially before release; AI security posture management, to show accumulated exposure; and AI detection and response, to intervene once an agent is live. The stated objective is moving custom agents into production with a defensible record of their behavior.