Agentic Autonomy Levels
TLDR
A framework for agentic AI systems categorizes autonomy levels from low (limiting risk and reversibility) to high (better for explicit tasks and parallel agent fleets). The frontier approach uses a manager agent that delegates to helper agents while verifying outputs and escalating only decisions requiring human judgment. This represents a design pattern for balancing AI autonomy with human oversight in multi-agent systems.
Why it matters
Low autonomy limits risk and increases reversibility, while higher autonomy is better for explicit activities and fleets of parallel agents. The frontier is the manager agent that delegates to helpers while verifying output and only returning decisions that must be made by humans.
