Governing AI agent work

Handing a ticket to an AI agent is easy. Knowing what it did — and stopping two agents from doing it at once — is the hard part. AgentTask treats governance as table stakes, not an enterprise add-on.

Claims: one worker per ticket

When an agent starts a task it takes a claim — a lease with a fencing token that other workers must respect. A second agent asking for the same ticket is told who holds it instead of silently duplicating the work. Claims heartbeat while work is live and expire if the worker dies, so stuck tickets free themselves.

Attribution: no anonymous writes

Every comment, status change, and edit resolves to a real actor: the OAuth user, or the creator of the API key the agent used. The platform refuses unattributed writes outright. When an agent posts an update, you can always answer who ran it, with whose access, on which ticket.

The audit trail is the product

Plans, runs, tool calls, and deliveries append to a trail that survives the task’s lifecycle. That turns “what did the agents do last week?” from an investigation into a query — and it is why teams can let agents touch production repos without holding their breath.

If your team is evaluating how to run agents on real work, this is the shape we’d argue for: shared backlog, claimed work, attributed actions, and a trail you can hand to an auditor.