Understanding how users interact with AI agents is crucial for optimizing customer experience. By collecting direct user feedback and analyzing conversational sentiment, teams gain valuable insights into what works well and where improvements are needed. With new customizable feedback collection and AI-powered sentiment analysis, organizations can measure user satisfaction more effectively, identify recurring issues, and refine AI responses to enhance engagement.

What This Change Enables:

• Customizable Feedback Collection Per Agent – Users can now enable a feedback toggle for each Generative Agent to gather insights from chat interactions. Admins can customize the feedback format, choosing between plain text responses or a 1-5 star rating system to suit their needs. Feedback is stored in a dedicated section within Conversation History, allowing teams to track trends and analyze user satisfaction. Additionally, conversations can be exported in HTML or CSV format, with an option to include or exclude feedback for reporting purposes.

• AI-Generated Sentiment Summary – When feedback collection is enabled, the AI automatically summarizes user sentiment as positive, neutral, or negative, displaying an icon in Conversation History. Full feedback details are accessible in the conversation detail modal, providing a clear picture of user interactions. Teams can use these insights to refine agent performance and ensure a more engaging experience.

These improvements provide deeper insights into customer interactions, making it easier than ever to refine AI agent performance and optimize user experiences.

Explore these new capabilities today. Learn more in our documentation.

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