Before an AI Agent Goes Live It Should Earn a Scope
Orgesa Berisha
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AI agents are increasingly able to answer questions, recommend actions and complete operational tasks.
But capability is not permission.
An AI agent may demonstrate that it can perform a task without proving that it can perform it consistently, safely and within the organization’s rules.
The question should therefore change from “Can the AI do this?” to “What evidence would allow us to trust the AI with this responsibility?”
Readiness should produce a defined qualification
Dr. Vigan Disha positions AI readiness as a qualification process.
An AI agent should not receive a general label declaring it ready. Its approval should define exactly where and how it can operate.
A practical AI-readiness record would include:
Readiness field | Required definition |
|---|---|
Approved task | The exact responsibility the agent may perform |
Knowledge source | The organizational information it may use |
Permitted channel | Where the agent may operate |
Performance standard | The accuracy and consistency required |
Confidence threshold | When the agent may act or respond |
Escalation condition | When the interaction must move to a person |
Review trigger | What changes require new testing |
This creates a visible boundary between demonstrated capability and authorized responsibility.
Test beyond the ideal scenario
A polished demonstration usually follows a predictable path.
Live operations do not.
Testing should include:
Normal customer situations
Ambiguous questions
Policy exceptions
Incorrect or incomplete information
Requests outside the agent’s scope
Sensitive decisions
Situations requiring human intervention
Dr. Disha’s approach treats these scenarios as readiness evidence rather than edge cases to be addressed later.
Match autonomy to risk
Not every task requires the same level of oversight.
Routine tasks
An AI agent may respond or act when the knowledge, process and expected outcome are clearly defined.
Uncertain situations
The agent may assist a person or recommend an action but should not operate independently.
Sensitive decisions
The agent should preserve the context and transfer responsibility to an authorized person.
The objective is not maximum autonomy. It is appropriate autonomy.
Approval needs an expiry condition
AI readiness cannot be permanent.
A change in policy, product information, regulation or customer expectations may invalidate the original evidence.
Organizations therefore need ongoing monitoring and clear triggers for retesting.
An agent may need new approval when:
Its knowledge changes
Its task expands
It enters a new channel
Performance falls below the required standard
The risk associated with the task changes
Accountability remains human
An AI agent may perform work, but the organization remains responsible for defining the conditions under which it operates.
Leaders need to know which knowledge produced an answer, why an action was recommended, how confident the system was and when human intervention was required.
Trust does not come from fluency.
It comes from evidence, transparency and control.
Before deploying the next AI agent, hear Dr. Vigan Disha explain how readiness, boundaries and human accountability fit together in What Comes After Readiness.
