Intelligent Repair Bots for Power BI: An Agentic Automation Approach

This article describes a new method of employing self-installing agents powered by Large Language Models (LLMs) in Power Automate on the cloud to solve issues relating to the verification of Power BI datasets by these agents. The descriptive name given to this innovative agent is "Agentic Repair Bots". The agents remain vigilant to monitor and track the connections made by the data in their respective pipelines. Two common problems frequently encountered by pipelines is the occurrence of authentication problems as well as a drift in schemas. Furthermore, agents, in order to rectify such issues immediately, trigger context-based pre-remediation scripts that allow for prompt resolution of issues, including credential refreshes and the mapping of new schema columns. Additionally, such monitoring and correction offer analysts as well as all users involved a significant cost reduction for their time that would have otherwise been spent requiring the manual intervention of analysts. Furthermore, this study identifies and describes the manner in which the Agentic Workflow Orchestration Architecture operates in a Mobile-Cloud environment and assesses the effectiveness of the agentic Workflow orchestration in reducing the mean time to resolution (MTTR).