Can one agent have different autonomy for different actions?
Yes, and it should. Reading data can be autonomous while writing or sending requires approval.
Agentic AI Orchestration · Autonomy
Choosing the Right Autonomy Level for AI Agents
Autonomy is not a property of the technology but a decision per process. These criteria make the decision explicit.
Decision criteria
- Impact of a wrong action on customers, money or compliance
- Reversibility of actions
- Quality measured on realistic scenarios
- Ability to detect errors quickly
- Capacity of people to review exceptions
Examples
| Process | Suggested level | Reason |
|---|---|---|
| Research briefs for sales | 3 | Low impact, easy to review |
| Customer refunds | 2 | Financial impact, approval needed |
| Data reconciliation between systems | 3 | Reversible corrections with logs |
| Contract commitments | 1 | Legal impact, human decision |
Moving to a higher level
Increase autonomy only after a period with stable metrics, documented exceptions and an agreed rollback plan. Keep sampling even at higher levels.
Moving back down
Lower the level immediately when quality drops, inputs change significantly or a model version changes, and investigate with traces before raising it again.
Designing oversight at each level
Oversight changes with autonomy. At levels 1 and 2 people review individual outputs. At level 3 they review exceptions and a random sample, which requires dashboards that show what the agent did and why. At level 4 oversight moves to process metrics and periodic audits. Plan the people and tools for oversight before raising the level, not afterwards.
Metrics that justify more autonomy
- Share of agent proposals accepted without change
- Severity and frequency of errors found in sampling
- Time to detect and reverse a wrong action
- Stability of results across model and prompt versions
Frequently asked questions
Who decides the level?
The process owner together with risk, security and data protection, based on measured quality.