Progressive delegation
Also known as: trust escalation, graduated autonomy, progressive autonomy
Most agent deployments fail not because the model is wrong but because users don't trust it enough to let it run. Progressive delegation is a design and deployment pattern that addresses that directly: you start the agent in a high-friction mode where it asks for approval on every significant action. As the user approves action after action, the system tracks that history and begins auto-executing routine tasks with a notification instead of a gate. The user's own behavior sets the pace of autonomy expansion.
The pattern is showing up across enterprise agent deployments in 2026. In one reported case, an operations team approved 40 consecutive pipeline reconfigurations suggested by an agent; at that point the system introduced auto-execution for routine changes, resulting in significantly higher adoption than versions that offered full autonomy from day one. The principle is that trust should be earned, not demanded, and the interface should make the earning process visible.
For builders, progressive delegation has a useful side effect: it generates labeled approval data. Every step a user approves or overrides is a signal about where the agent's judgment is reliable and where it isn't. That data feeds back into evals (automated tests of model behavior) and can inform future fine-tuning or policy rules. The agent gets smarter about what it can do autonomously, and the builder gets a clearer picture of where human oversight is still necessary.