Abstract: A method is provided. The method is performed by a hyper-automation system performing agentic automations. The hyper-automation system include a memory storing for the agentic automations and for agent health score generation. The hyper-automation system include at least one processor executing the computer code to cause the method to optimize the agentic automations. The method includes generating a set of standardized metrics for application to an agent executing agentic automations. The method includes performing an evaluation of the agent executing the agentic automations that determines performance metrics by measuring success rates, error frequencies, and execution efficiency for different use cases or business processes. The method includes comparing the performance metrics across different use cases or business processes for the agent. The method includes generating agent health scores based on comparing the performance metrics and agent health score definitions.

Abstract: A method is provided. The method is executed by an AI powered assistant engine implemented as a computer program within a computing environment. The AI powered assistant engine provides a co-pilot guided experience and automation using contextual knowledge. The method includes receiving inputs from a user via a user interface initiating a conversation, providing AI models based on the inputs, accessing knowledge repositories representing neurons to provide and augment the contextual knowledge generated by the knowledge repositories in response to the inputs, and generating recommendation responses to the inputs as the co-pilot guided experience by the AI models processing the contextual knowledge in an overall decision-making structure.