AI governance · 9 min read
Conversational AI agents: where should human supervision sit?
A decision framework for automating repetitive requests without letting the agent act beyond its mandate.
By Équipe Lina
Autonomy must be defined by situation
A conversational agent should not have one autonomy level for every task. Answering opening hours, changing a delivery address and approving a refund expose the company to different risks. Governance therefore starts with a decision matrix, not an on-or-off switch.
For each intent, define accessible data, permitted actions, validation conditions and the fallback for uncertainty. This makes the system understandable to business teams and auditable.
Three levels of human involvement
At the first level Lina answers and acts within a low-risk scope. The second requires validation before sending or executing. The third transfers immediately with a summary and recommendation.
The right level depends on the impact of an error, data sensitivity, reversibility and measured confidence in the detected intent.
- Autonomous: approved FAQs, order status and information collection.
- Assisted: commercial gestures, contract changes and sensitive replies.
- Transferred: threats, disputes, medical or legal data and high uncertainty.
A handoff must carry more than one message
An operator should not reread twenty messages to understand the situation. The handoff should include detected intent, collected facts, consulted sources, completed actions and the reason for escalation. The original conversation remains available to verify the summary.
This structure improves takeover speed and makes transfer causes measurable. It also prevents customers from repeating information they have just provided.
Control through sampling and exceptions
Quality control should combine a regular sample with targeted alerts: low confidence, aggressive language, commercial commitments, personal data or unusually long sequences. Observed failures feed rules, content and regression tests.
Useful governance does not block automation. It makes the scope explicit, measurable and adaptable, allowing autonomy to increase when quality evidence is strong enough.