About the project
Building one AI agent is easy. Running dozens across a company is not: someone has to decide who can act, on which data, at what cost, and who approves the risky steps. GOAT handles all of that in one place. Employees keep asking for help where they already work, in Slack or Teams, and GOAT checks every request behind the scenes.
The challenge
Companies want AI agents in their daily work, but their security and compliance teams keep asking the same questions. Can an agent reach data it shouldn't? Who approved this change? Can we prove it afterwards? Most agent tools leave those answers to policy documents and good intentions.
The solution
GOAT sits between your people, your AI agents and your systems. Every request goes through the same checks, every time, and nothing can skip them.
Clear rules before every step
You decide what each agent may do. If no rule allows an action, it doesn't happen.
Only the data the task needs
Agents never get open access to your systems. GOAT gives them just what the task requires.
A person for risky actions
Sensitive steps wait for approval, and the approver sees exactly what they are signing off.
The right AI model for the data
GOAT decides which AI model may see which data, so sensitive information stays where it belongs.
A complete record
Every request, decision and approval is logged, so you can always answer "who let this happen?"
Runs where you need it
In the cloud, in your private environment, on your own servers, or fully offline. The checks and the record stay on in every setup.
How it works in practice
An employee writes in Slack: "Reset my MFA." GOAT checks the rules and passes the request to the helpdesk agent with only the data it needs. Because an MFA reset is a sensitive step, it waits until the employee's manager approves it. Only then does the change go through, and every step is on record.