Glossary
Graduated autonomy
What is graduated autonomy?
Graduated autonomy means giving an AI agent more responsibility in steps as it proves itself on real work: from shadow mode, to suggesting while a person approves, to acting with spot checks, to acting alone on low-risk cases. It moves back down when quality slips.
Also called: Autonomy ladder
Why it matters
It replaces an all-or-nothing launch with evidence at every step. Each promotion rests on the agent’s record on your own cases, and a serious error, or a change of model or prompt, moves it back down.
Example
A deal desk agent spends four weeks in shadow mode, then drafts every summary for an approver to sign off. Multi-currency deals stay with people until it passes on those too.
Learn more
Guide · ProveWhat is shadow mode? Testing AI agents on real work before they go liveRun an agent on real work without sending anything, score it against your people, and promote it one step at a time.Guide · ProveAI agent observability and governance: how to monitor agents after go-liveWhat to record for every agent run, what to watch after go-live, and the controls and frameworks that keep agents accountable.Guide · EquipSidekick agents vs. background agents: which work should each do?Which work suits an agent at your side and which suits one that runs on its own, plus approvals, risks and controls.