Govern every action your AI agents take.

Agent Gate is a platform for connecting AI agents to enterprise tools securely and at scale. Product and engineering teams get maintained connectors, authentication, and observability without building integration plumbing themselves. IT and security teams get a governance layer over employee AI: identity-based provisioning, policy enforcement on every tool call, and a searchable record of what agents actually did. Individuals can install Agent Gate as a remote MCP server in Claude, Cursor, ChatGPT, or any MCP-compatible client, then authorize the connectors they want their agent to reach. It's built on MCP and works with any agent framework.

Security is part of the architecture, not an add-on. Agent Gate handles credential storage and token refresh for you, scans tool-call inputs and outputs for sensitive data, and applies policy rules that can block, mask, or redact content before it reaches a model or an external system. Every call is logged.

You can start for free. Paid plans are usage-based: each account gets a monthly credit allowance that's drawn down as agents make tool calls. Higher-volume and seat-based arrangements are available on enterprise plans. See the pricing page for current tiers.

First, IT and security teams at companies where employees are already pointing AI tools at internal systems, and who need centralized access control, data-loss prevention, and an audit trail. Second, engineering and product teams shipping agent-powered features, who need dependable connectors and don't want to own integration maintenance. Third, individual developers and power users who want their everyday AI clients to act on the tools they actually work in.

MCP is the protocol agents use to call tools. Agent Gate is what makes that protocol safe to run in production. Most public MCP servers are built fast and left alone: thin error handling, no auth story, no visibility into what happened. Agent Gate supplies the layer around them - maintained connectors with real error semantics, managed credentials, data scanning on every call, scoped permissions, and searchable logs. Instead of sourcing and babysitting a separate server per tool, you get one governed entry point for all of them.

Three main jobs. Building agent products: ready-made connectors to Salesforce, Jira, Slack, GitHub, HubSpot and hundreds of others, with auth, permissions, and monitoring in one place. Governing employee AI: centralized provisioning, data-loss prevention on every call, and a complete trail for compliance and incident review. Personal setups: run it as an MCP server in your preferred AI client, authorize what you want, and let your agent take real actions on your behalf.

It sits between the AI tools employees use and the systems those tools reach, enforcing policy at the moment of the tool call - so nothing about employees' day-to-day workflow has to change. Provisioning connects to Okta, Entra ID, or any SCIM-compatible identity provider, so tool access follows your existing directory. Guardrails inspect both the request and the response for sensitive data, and can block, redact, or simply log based on rules you define. The audit trail captures who initiated each action, what arguments were passed, which downstream APIs were hit, and what came back - all filterable for reviews and investigations.

Yes. Add it as a remote MCP server in Claude, Claude Code, Cursor, ChatGPT, or any client that supports remote MCP. Create a user, assemble the tool bundles you want, and authorize the connectors your agent should be able to reach. Personal use runs on the same free and pro tiers as everything else - there's no separate signup or plan to figure out.