Endpoint security for AI agents covering discovery, runtime guardrails, and MCP governance

Autonomous Security, based in New York, works on a problem that arrived faster than the controls for it: developers and knowledge workers have installed AI agents and coding assistants on corporate laptops, wired them to MCP servers and plugins nobody reviewed, and handed them credentials with real privileges. Traditional endpoint tooling sees a signed binary making ordinary API calls and has nothing to say about it. Autonomous instruments the workstation to watch agent and model activity directly, producing an inventory of which agents are present, what skills, plugins, and MCP servers each one loads, and which secrets and configurations are sitting exposed where an agent can read them.

The platform is arranged in three layers. Discovery maps the AI tools running across the fleet, including ones deployed without approval, and analyses the exposure each creates, with credential theft through a compromised or malicious extension treated as the primary supply chain risk. Runtime protection applies guardrails in-line, so an agent action that falls outside policy is stopped before it executes rather than reported afterwards. A secure MCP cloud handles the governance side, managing policy, identity, and authentication for the MCP servers an organisation sanctions. Output feeds existing infrastructure through SSO, SCIM, and SIEM integrations, and produces documentation intended for audit. The company says it tracks more than fifty agents in common enterprise use.