Agent and client identity
Give every machine client its own identity, credentials, status, and access lifecycle.
A modern policy and orchestration control plane.
AI Controller gives your agents controlled access to the apps you want them to use. Agents connect to the controller through MCP, where you choose exactly what each one can do. The controller verifies the agent, checks every request against your policy, then runs and records approved actions. Requests outside your policy are blocked, protecting your apps and data from unpredictable agent behavior. Built in Go for fast, lightweight performance.
Runs on Windows, Mac, Linux, and your own servers.
The problem
Everyone treats AI as a magical oracle on a mountaintop where you ask a question and it hands back the answer.
But answers are not the real opportunity. The real value comes when AI can use your systems, complete a workflow, and return finished work.
That requires access to email, files, databases, infrastructure, and business applications. Connecting an agent directly to all of it creates a new problem: credentials spread across tools, permissions become difficult to reason about, and important actions happen without one place to control or review them.
The answer is not to keep AI trapped in a chat window. The answer is to give it a controlled way to act.
The control layer
AI Controller sits between the AI and the systems where work happens. Agents ask for a capability. AIC verifies the client, checks what it is allowed to use, executes through the correct plugin, and records the result.
The agent gets the outcome it needs. It does not get the underlying password, token, connection string, or unrestricted access to the system behind it.
Your agents can move faster because control, credentials, execution, and visibility live in one place.
How it works
Register OpenClaw, an MCP client, a custom agent, or a business application with its own identity.
Install and configure plugins for the tools, data, and infrastructure the agent needs to reach.
Choose which client can use which capabilities. Disable, revoke, or rotate access without reworking every agent.
AIC executes the request through the controlled connection and records the activity, outcome, and source.
What AIC controls
Give every machine client its own identity, credentials, status, and access lifecycle.
Keep connector secrets inside the controller. Agents receive authorized outcomes, not the credentials behind them.
Grant only the tools each client needs, then enable, disable, rotate, or revoke access centrally.
Connect business systems through explicit capabilities instead of giving agents broad network or account access.
See authentication activity, tool calls, process activity, completed work, denials, and operator changes in one console.
Use MCP for agent discovery and bounded tool calls, with secure APIs for applications and durable operational work.
Built for action
OpenClaw, Claude, ChatGPT, Cursor, or your own application can reason about the work and request the next action. AI Controller owns the execution boundary: authentication, authorization, credentials, connectors, runtime controls, and records.
That separation lets you change agents and models without rebuilding every integration or distributing powerful credentials across every AI tool.
AI agents and applications
Your tools, data, and infrastructure
Tell me what your agents need to reach and what must stay protected. I will show you how AI Controller can put a secure execution layer between them.
Talk About Your SetupBuilt for controlled access, protected credentials, and accountable execution.