Are Data Store Agents and Playbooks self-learning from user interactions?

[abbynormal]

Do Google's generative AI agents (Data Store/Playbooks) built in Conversational Agents console learn from user interactions (e.g., thumbs up/down, common questions) to improve responses? If so, I would greatly appreciate links to relevant documentation and more detailed information regarding this. Thanks. 🙂

[MarvinLlamas]

Hi @abbynormal,

Welcome to the Google Cloud Community!

No, Data Store Agents and Playbooks do not autonomously or continuously adapt to user interactions, such as thumbs up/down or specific questions, in the same way a model fine-tunes itself in real-time.

While Google's Data Store Agents and Playbooks don't learn automatically from user interactions in real-time, you could try the following steps to enhance their performance:

• Improving the Data: You may want to improve your Data Store Agent's performance by enhancing the quality, relevance, and structure of your data. This includes cleaning up your existing data, adding more relevant information, ensuring proper indexing, and organizing your data in a way that the agent can easily process and understand.
• Refining Search Configuration: You may want to adjust your Data Store Agent's search configurations, such as prioritizing your preferred fields or weighting your data sources differently. This involves you manually analyzing your users' behavior and addressing your common search failures.
• Using Feedback to Identify Areas for Improvement: You can use user feedback (thumbs up/down) and analyze your users' common questions to identify gaps in your data or areas where your search configuration needs improvement. This helps you make manual adjustments but doesn't automatically trigger retraining of the agent's model.

Was this helpful? If so, please accept this answer as “Solution”. If you need additional assistance, reply here within 2 business days and I’ll be happy to help.