Lunen.ai

Most enterprise AI governance layers arrive after the fact — bolted onto agents that were already built in a tool IT never approved. Lunen.ai inverts that order. A non-technical user describes an agent in plain language; Lunen generates a structured execution plan with specific tool calls, data scopes, and a schedule. That plan is also the governance artifact: the same document that gets reviewed is the one that runs. Supported integrations include Atlassian, BigQuery, Google, HubSpot, Slack, and any MCP server.

The differentiating feature is per-tool policy control applied uniformly across every agent and every ad-hoc run. Each MCP tool in a connection gets its own toggle: ‘Allow’ lets the agent call it unattended; ‘Approve’ pauses execution and presents the inputs to a reviewer before the call fires. This means a team can let an agent read CRM records freely while requiring a human sign-off before it creates or updates anything — without writing separate enforcement logic for each agent.

Lunen fits organizations where the primary obstacle to production deployment is clearing a security or compliance review, not engineering complexity. Teams that need branching logic — ‘if step three returns X, go to step four-A; otherwise step four-B’ — will find that the plain-language plan model does not make those conditional paths visible or configurable through the interface. At that point the team is either simplifying the agent to fit the model or building the branching logic outside Lunen, which reintroduces the two-system problem the platform exists to eliminate.

Deployment is cloud or BYOC (bring your own cloud); the vendor states custom retention and deployment options are available for enterprise requirements. The audit log consolidates both user-initiated and agent-initiated actions into a single event stream, with each event expandable to show who acted, what inputs were passed, and what ran — the format the vendor describes as designed to be defensible in a security review.