Verified completion: AI agents that can't lie about finishing

The most common one-star review of autonomous-agent products says the same thing: "it said the task was done — it wasn't." Agents mark work complete that never deployed, burn credits on failed actions, and bury failures inside cheerful summaries. The model isn't malicious; it's just ungrounded — nothing forces its claim of "done" to match reality.

How KentoHQ makes false completion impossible

In KentoHQ, an agent cannot simply declare success. To finish a task it must submit machine-checkable proof: files that must exist, commands that must pass, pages that must respond, or a strict independent AI judge grading the deliverable against criteria. The engine — not the agent — runs those checks. If they fail, the task goes back. If the agent keeps claiming falsely, the task is marked not done, honestly.

And one more pass for quality

Even when the checks pass, the engine demands a self-review: re-read the brief, compare with the owner's reference material, improve if possible. Only then is the work accepted — and you can read every step the agent took, in plain language, in the task's conversation view.

That's the whole philosophy: trust is the product. See it work →