Agent Quality Chat Metric

Evaluate the AI assistant’s performance based on helpfulness, accuracy, and appropriateness across the full conversation, giving a holistic engagement score.

The Agent Quality metric evaluates the accuracy, relevance, and contextual alignment of responses generated by the conversational agent in a chat use case. This metric assesses whether the agent’s responses fulfill the user's intent, maintain engagement, and provide appropriate and coherent replies within the conversation flow. An LLM is used as an evaluator to determine if the agent’s responses meet quality and engagement standards for an effective chat interaction.

Required Column in Dataset:

• Chat: A single column containing both user prompts and the agent’s generated responses in a sequential format, capturing the full conversation context.

Interpretation:

A higher Agent Quality score suggests that the agent's responses are more contextually appropriate, aligned with the user’s intent, and contribute positively to the conversation's flow. This metric reflects the agent’s ability to maintain high-quality interactions in chat settings.

Metric Execution via UI:

To execute this metric, select Agent Quality from the list of metrics in the UI and configure evaluation settings to assess responses within the conversation sequence.

Example:

• Chat:

  • User: “Can you help me find a good Italian restaurant nearby?”

  • Agent Response: “Sure! There’s a highly-rated Italian restaurant nearby called ‘La Dolce Vita,’ known for its authentic pasta and friendly atmosphere.”

• Metric Score: 0.95 Reasoning: The agent’s response is accurate, aligns with the user’s intent, and provides a helpful recommendation. The response includes specific details about the restaurant, contributing positively to user engagement in the conversation.