Real-time authorisation controls for autonomous AI agents.
AgentGuard sits between your AI agents and your infrastructure. Every command an agent issues is classified, authorised, or blocked at runtime — before it touches your filesystem, your APIs, or your data. Sub-millisecond decisions. No observable latency.
AgentGuard operates as a daemon between the AI agent runtime and the operating system. Every command — whether issued via shell, library function, or remote procedure call — is evaluated against the trained classifier before any system effects occur. Decisions are logged in full, with structured provenance, and surfaced through the integration of choice.
An autonomous agent attempts to issue a command in the customer environment.
The classifier evaluates the command across thirteen risk categories in under 250 microseconds.
Block at syscall, flag for human approval, or authorise with audit log. All outcomes are signed and logged.
Risk categories are derived from analysis of more than 200,000 production agent commands across customer engagements, public corpora, and red-team exercises. Each category carries an assigned severity tier that maps to a default authorisation policy, which customers may override per-environment.
AgentGuard is available in three editions distinguished primarily by deployment surface, update cadence, and support model. The Community edition is open source; Professional and Enterprise editions are commercially licensed and include managed updates and direct support.
AgentGuard is designed to integrate with existing agent runtimes without modification to application code. Reference integrations are maintained for the most commonly deployed agent frameworks; additional integrations are developed under engagement with our platform team.
Most engagements begin with a structured evaluation against representative agent traffic from your production environment. Our team can provide reference deployments, severity policy tuning, and integration support throughout.