Abstract
A learning agent designed to automate the root cause analysis (RCA) of failures within complex distributed systems. The agent can be configured to perform iterative generation and investigation of hypotheses. When a service failure alert is triggered, the agent may ingest data from various sources, including live monitoring data and historical incident records. Based on this information, the agent may be configured to formulate a ranked list of potential hypotheses for the cause of the failure. The agent then may be configured to systematically investigate each hypothesis by gathering and analyzing evidence from relevant data sources until a root cause is validated.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
N/A, "SELF-LEARNING, ITERATIVE AGENT FOR ROOT CAUSE ANALYSIS OF SERVICE FAILURES", Technical Disclosure Commons, (September 04, 2025)
https://www.tdcommons.org/dpubs_series/8551