Plain-language agent planning
Users can describe an agent in plain language and Lunen drafts a structured execution plan with named tools, scoped data, and a schedule.
Lunen
ReclamarLunen is a governed control plane for AI agents across business systems. Approve actions, control permissions, and keep a shared audit record in production.
AI agent governance for production teams
Lunen is a governed control plane for building and running AI agents across existing business systems. It is positioned for teams that want to let people create agents in plain language while retaining approval steps, tool-level permissions, and a shared audit record.
The product is framed around a specific workflow: someone describes an agent, Lunen turns that request into a structured plan, and the team decides whether each tool call can run unattended or must wait for approval. The pricing page shows an Operational Control plan for teams in production and an Enterprise plan for organizations that need dedicated deployment, private networking, and extended retention.
Core capabilities
Per-tool approval controls
Teams can set each tool to run unattended or require a human approval before each call, so access rules can vary by agent and system.
Unified audit trail
User actions and agent actions roll up into a shared audit log that records who acted, what was approved, what model ran, and which data it touched.
Role-based governance controls
The pricing page includes role-based access control and an allow/approve policy engine for production governance.
Connected-system workflows
Lunen supports connected systems through MCP, and the homepage examples reference Atlassian, BigQuery, Google, HubSpot, Slack, and any MCP server.
Deployment options for team and enterprise use
Operational Control is offered as a multi-tenant cloud deployment, while Enterprise adds dedicated deployment or BYOC and private networking.
Common workflows
Automated marketing triage
A marketing team can create a lead-scoring agent that pulls recent leads, reviews engagement history, ranks prospects, and posts a summarized list to Slack on a schedule.
Operational reporting
A customer success or revenue operations team can ask for a digest of closed-lost deals, then have the agent gather notes and call logs before posting the result to Slack.
Controlled access to sensitive systems
A legal or security team can review every tool call before it runs, using approval gates and the audit log to keep production systems reviewable.
Governance across agents and ad-hoc actions
An organization adopting MCP-connected tools can apply one policy layer across agents and ad-hoc runs so the same governance rules govern both planned and one-off actions.
Phased production rollout
A team evaluating enterprise rollout can start with Operational Control in multi-tenant cloud, then consider Enterprise when dedicated deployment, private networking, or extended retention are required.
Pros and Cons
Pros
- Combines agent creation and governance in the same workflow rather than splitting them across separate systems.
- Supports plain-language planning, which lowers the setup burden for subject-matter experts.
- Lets teams apply approvals at the tool level instead of relying on an all-or-nothing security posture.
- Keeps user and agent actions in one audit log for review and export.
- Offers a production-oriented team plan plus an enterprise option with deployment and networking controls.
Cons
- The source does not provide a complete integrations list or documentation for every connector.
- Pricing is still in early access, so some plan details and limits are being set with the first customers.
- The solo plan is not available yet; the company says it is starting with teams first.
FAQ
What is Lunen used for?
Lunen is designed for teams that want to build and run AI agents on existing systems while keeping approvals, permissions, and audit history under control. The source describes it as a governed control plane for enterprise AI adoption.
How do teams build agents in Lunen?
The homepage describes a plain-language workflow: a subject-matter expert describes an agent, Lunen drafts a structured execution plan, and the team reviews it before saving and running it. The plan can include named tools, scoped data, and a schedule.
How does approval and governance work?
Lunen applies policies at the tool level. For each MCP tool, teams can allow it to run unattended or require a human approval before each call, and the same policy applies across agents and ad-hoc runs.
What plans does Lunen offer?
The pricing page shows Operational Control for teams and Enterprise for the org-wide control layer. Enterprise adds dedicated deployment or BYOC, private networking, extended and regulated audit retention, custom tool-call volume, and dedicated support with an SLA.
Is Lunen generally available?
The source says Lunen is in early access and is working with a small group of design partners. It also states that the team will tell prospects honestly whether Lunen fits and shape the plan together if it does.
Quick Facts
- Category
- AI agent governance platform
- Primary users
- Teams running agents in production; enterprise organizations
- Deployment
- Multi-tenant cloud for Operational Control; dedicated deployment or BYOC for Enterprise
- Control model
- Role-based access control, allow/approve policy engine, shared audit log
- Connected systems
- MCP-based workflows; homepage examples include Atlassian, BigQuery, Google, HubSpot, and Slack
- Company framing
- Now accepting design partners; early access
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