Lunen.ai
Summary
Enterprise AI pilots keep dying in the gap between the team that builds agents and the team that has to approve them — two tools, two approval paths, and a governance layer nobody actually uses. Lunen.ai is built to close that gap by making agent creation and agent control the same motion.
A subject-matter expert describes what they want in plain language; Lunen drafts a structured execution plan with named tools, scoped data, and a schedule — no canvas, no YAML. Every MCP tool connection becomes a per-tool policy decision: allow it to run unattended, or pause for a human sign-off before each call. User actions and agent actions land in the same audit log, which means security reviews have a single trail to pull. The ceiling appears when teams need conditional branching between agent steps — the plain-language plan model does not surface that logic visibly, so complex multi-step dependencies require workarounds the interface does not directly support.
Bottom line: Pick Lunen when your blocker is getting agents through a security review and into production; look elsewhere when your agents need conditional branching logic that a plain-language plan cannot express.
Pricing Plans
SubscriptionOperational Control
Production governance for teams; 50,000 tool calls/month included; role-based access control; 90-day audit retention; multi-tenant cloud
- Role-based access control
- Allow/approve policy engine
- Unified audit log
- Priority email + Slack support
Enterprise
Org-wide control layer; dedicated deployment or BYOC; extended audit retention; private networking; SLA support
- Everything in Operational Control
- Dedicated or BYOC deployment
- Private networking
- Dedicated SLA support
View full pricing on lunen.ai →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Plain-language agent creation produces a structured execution plan without drag-and-drop builders or YAML, so non-technical staff can define agents that IT can actually review and approve rather than shadow-deploying on personal accounts.
- Per-tool allow/approve toggles apply to every agent and every ad-hoc run from a single policy screen, which means a CRM write permission cannot accidentally slip through on a one-off run that bypasses the standing policy.
- User actions and agent actions land in the same audit log with full input visibility per event, so compliance teams pull a single trail instead of reconciling agent logs against user logs during a review.
- MCP server support means the policy and audit framework extends to any tool with an MCP integration, not just the named connectors — reducing the risk that a new integration creates an ungoverned side channel.
- BYOC deployment keeps production data inside the organization's own infrastructure, which means data residency requirements do not force a choice between governance tooling and compliance posture.
Cons
Sign in to edit- The plain-language plan model has no visible mechanism for conditional branching between steps — if an agent needs to take different paths depending on what a prior step returned, the interface gives no way to express or inspect that logic, and teams handling multi-step decision trees will route around Lunen with external orchestration, reintroducing the two-system problem.
- There is no free tier; access is gated behind a paid plan or an enterprise contact-sales path, which means teams that want to evaluate the governance model against a real production workflow before committing budget have no low-friction entry point — the evaluation friction alone pushes some teams toward open-source alternatives where they can self-host and test without a contract.
- The tool set is limited to named connectors plus MCP servers; organizations running internal tooling without MCP support face a build-your-own integration problem that sits outside the governed plane Lunen provides, leaving those tool calls unlogged and unapproved.
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About
- Platforms
- Cloud
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-07-20T20:30:55.246Z
Best For
Who it's for
- Teams deploying agents in production
- Organizations requiring policy-based agent control
- Enterprises needing custom deployment and retention
What it does well
- Running production AI agents with approval workflows
- Governing agent actions across connected systems like Slack and GitHub
- Maintaining audit logs for security and compliance reviews
Integrations
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Frequently Asked Questions
- Is Lunen.ai free?
- Lunen.ai is a paid tool. No permanent free tier is offered.
- Is Lunen.ai open source?
- No — Lunen.ai is a closed-source tool. Source code is not publicly available.
- Can I self-host Lunen.ai?
- Yes. Lunen.ai supports self-hosting on your own infrastructure.
- When was Lunen.ai released?
- Lunen.ai was first released in 2026.
- What platforms does Lunen.ai support?
- Lunen.ai is available on: Cloud.
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Curated lists that include this category
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.