IR Operator

An active research direction; we are still defining the boundaries of what an AI operator can safely do.

What does it take for an AI to operate real software reliably — and how much approval should it ask for?

IR Operator explores giving an AI agent the ability to carry out tasks inside real applications: understanding the task, acting on the interface, requesting approval at key steps, and verifying the outcome before calling it done.

• Translate a task into concrete steps the software actually supports.
• Interact with the real application — UI or API — step by step.
• Pause and request approval before any irreversible action.
• Verify the result against the original request before reporting done.

• Where an operator model beats simple chat: real actions, real outcomes.
• How much guard-railing is needed before an agent can be trusted.
• The minimum verification loop that makes an operation reliable.

IR Operator is an experiment in early stages: prototypes interact with controlled applications under supervision. It is not deployed on production systems.

• Current prototypes work on controlled, predictable interfaces.
• Anything irreversible still requires a human decision.
• No production data is handled by the experiment.

• Verification matters more than generation: an agent that checks its own work is safer.
• The boundary between “do it” and “ask first” is the product, not a detail.