Abstract
This disclosure describes techniques for enhancing search engine results pages (SERPs) by employing an agentic AI framework. A framework is presented that analyzes user queries to determine the optimal presentation format for the response. For certain predefined query types (e.g., real-time data like stocks, sports scores), the framework retrieves existing interactive widgets. For other queries deemed suitable, the framework dynamically generates new, bespoke interactive widgets or simple web applications ("micro-apps") tailored to the detected intent of the query. This generation process involves the use of specialized artificial intelligence (AI) agents collaborating to design, build, fetch data for, and verify the functionality and user experience of the generated content. Generated content can be initially deployed to a small user fraction, with rollout scaled based on automated monitoring of user interaction metrics and explicit feedback, ensuring quality and relevance. The described techniques can incorporate personalization based on user history and preferences (both accessed with specific user permission) and leverage cloud-based AI agent platforms for implementation, including agent-to-agent communication protocols.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Chugh, Tushar; Mone, Aditya; and Kumara, Karthik, "Agentic AI Framework for Dynamic Generation, Verification, and Presentation of Interactive Content in Search Results", Technical Disclosure Commons, (June 25, 2025)
https://www.tdcommons.org/dpubs_series/8279