Before expanding what an AI agent can do, I would write down what it is allowed to decide.
An agent that summarizes a document has a different consequence boundary from one that changes a customer record. Giving both access to the same underlying model tells us very little about how they should be governed.
Define the operating boundary
For each workflow, I would define:
- What the agent may read.
- What it may propose.
- What it may change without further approval.
- What must be escalated.
- How an incorrect action is detected and contained.
Test the inconvenient cases
Then I would test the edges. An ambiguous request. Missing evidence. A tool returning incomplete data. A task that changes halfway through execution.
The objective is to understand whether the system stays within its authority when the situation becomes less convenient than the demo.
This gives engineering and business owners something concrete to review together. A capability can earn broader authority as evidence improves, while consequential actions retain the controls their risk requires.
Autonomy becomes a set of deliberate operating decisions. That is a much more useful starting point for an enterprise rollout than a single label on a product slide.
Where does your agent’s decision-making authority end—and is that boundary actually tested?