Agentic AI introduces a different engineering problem from conventional prediction models.
A predictive model produces an output.
An agent can potentially use that output to interact with tools, retrieve information, modify systems, communicate with users, or initiate workflows.
This creates an important architectural distinction between capability and authority.
An AI system may have the technical capability to call an API without having authorization to perform every operation exposed by that API.
Healthcare AI should therefore implement explicit decision boundaries.
One useful way to think about these boundaries is through levels of activity.
The system may observe information, generate an interpretation, recommend an action, request authorization, or execute an approved action.
These stages do not need to have the same permission level.
For example, an AI assistant might be permitted to retrieve relevant clinical information and prepare a recommendation while requiring explicit authorization before performing a consequential action.
Authorization should ideally be enforced technically.
Role-based access control, scoped credentials, approval gates, tool restrictions, audit logs, and policy enforcement can prevent an AI system from exceeding its intended authority.
This is different from simply asking the model to behave responsibly.
A prompt can describe a boundary, but an architectural control can enforce one.
This distinction becomes increasingly important as AI systems gain access to external tools.
The engineering objective should not be unrestricted autonomy.
It should be controlled autonomy, where the system's capabilities, permissions, and consequences are deliberately aligned.
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