Add negative examples to the None intent training set is the correct action to reduce the chance that unrelated user messages are incorrectly classified as that intent.

Adding negative examples to the None intent training set teaches the classifier which utterances should not map to the RetrieveSupportContact intent. This improves the model's precision because the classifier learns boundaries between the target intent and unrelated queries when you include representative negative examples and then retrain the agent.

Increase the intent classification confidence threshold is not the best choice because changing a runtime threshold can reduce false positives but it also increases the chance of false negatives and it does not improve the underlying model. It is a blunt instrument rather than a training based fix.

Create a machine learned entity is incorrect because entities are used to extract parameters from a matched intent and they do not influence whether an utterance is classified into a particular intent. Building an ML entity does not address misclassification of whole intents.

Enable active learning is not the immediate solution because active learning helps you collect and label ambiguous or high value examples over time. It does not by itself stop current unrelated messages from being classified as the intent until you review and add those examples to the training set.

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