Reduce recurring issues and incidents by remediation artificial intelligence

A system comprising:

• a non-transitory memory storing instructions;
• and
• one or more hardware processors coupled to the non-transitory memory and configured to read the instructions from the non-transitory memory to cause the system to perform operations comprising:
• determining, using a first generative artificial intelligence (AI) model, adverse trends from at least one issue metric;
• identifying a plurality of issues corresponding to the adverse trends in a computing environment;
• determining, using a first AI model in an ensemble of generative AI models, a plurality of root causes from the plurality of issues;
• determining, using a second AI model in the ensemble of generative AI models, impacted area information and process information from the plurality of issues;
• determining, using a third generative AI model in the ensemble of generative AI models, the plurality of root causes, the impacted area information and the process information, at least one recommendation for rectifying the plurality of issues;
• generating a traversable network graph comprising the plurality of root causes, the impacted area information, the process information, and the plurality of issues, wherein the impacted area information comprises impacted systems and products, and process information comprises impacted processes or functions;
• and
• displaying the traversable network graph on a user interface.

A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

• determining, using a plurality of large language models (LLMs), adverse trends from at least one issue metric, wherein the at least one issue metric is associated with issues occurring in a computing system;
• identifying a plurality of issues corresponding to the adverse trends in the computing system;
• determining, using the plurality of LLMs, a plurality of root causes from the plurality of issues, wherein the determining further comprises:
• generating, using a first LLM in the plurality of LLMs, similar historical issues from the plurality of issues;
• generating, using a second LLM in the plurality of LLMs and in parallel with the first LLM, root cause information from the plurality of issues;
• and
• generating, using a third LLM in the plurality of LLMs, the plurality of root causes for the plurality of issues from the similar historical issues and the root cause information;
• determining, using the plurality of LLMs, impacted area information from the plurality of issues in parallel with determining process information from the plurality of issues;
• determining, using the plurality of LLMs, at least one recommendation for rectifying the plurality of issues from the plurality of root causes and the impacted area information and the process information;
• and
• generating a traversable network graph comprising the plurality of root causes, the impacted area information and the process information, and the plurality of issues.