Abstract
A method is described for an artificial intelligence agent to perform an autonomous research cycle. A system can enable an agent to periodically retrieve new information from a dynamic data source and use it to construct and continuously update an internal knowledge graph. The agent may then autonomously invoke a set of analytical modules to analyze this graph. Such modules can perform functions including trend analysis, knowledge synthesis, and hypothesis generation, potentially without direct human intervention. This process can modify an agent's behavior from a reactive model to a proactive one, allowing it to independently discover and surface unsolicited insights, emerging trends, and knowledge gaps to a user.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kumar S R, Mithun; Innocent, Graham; and Zhuravlev, Aleksei, "Autonomous Research Cycle for Artificial Intelligence Agents Utilizing a Dynamic Knowledge Graph", Technical Disclosure Commons, (October 22, 2025)
https://www.tdcommons.org/dpubs_series/8763