Artificial intelligence framework for system forensics

A computer system for troubleshooting and improving software programs or physical systems with continuous learning, the computer system comprising:

• a plurality of processors that, when executed, are programmed to perform:
• receiving target-system multimodal event data that was previously generated from execution of a target system, wherein the target-system multimodal event data includes diagnostic data, telemetry, and configuration data;
• providing a lifelong learning repository that includes previously-stored multimodal data and previously-stored detected anomalies from prior executions of the target system or other target systems;
• delivering the target-system multimodal event data to an orchestrator;
• utilizing the orchestrator to manage a plurality of artificial-intelligence (AI) agents, wherein the orchestrator selects, based on the target-system multimodal event data, a subset of the AI agents to perform operations including:
• detecting an anomaly in the target-system multimodal event data,
• accessing the lifelong learning repository of the previously-stored multimodal data and the previously-stored detected anomalies,
• performing specialized tasks tailored to that AI agent regarding the detected anomaly based on the previously-stored multimodal data and the previously-stored detected anomalies in the lifelong learning repository to identify candidate root-causes of the detected anomalies, and
• delivering an outcome of the specialized tasks to the orchestrator;
• and
• at the orchestrator, determining a recommending resolution to the detected anomalies based on (i) the outcomes from the subset of AI agents and (ii) the previously-stored multimodal data, the previously stored detected anomalies, and previously-computed model weights in the lifelong learning repository.

A non-transitory computer-readable storing medium storing instructions that, when executed by a processor, cause the processor to perform steps comprising:

• receiving target-system multimodal event data that was previously generated from execution of a target system, wherein the target-system multimodal event data includes diagnostic data, telemetry, and configuration data;
• providing a lifelong learning repository that includes previously-stored multimodal data and previously-stored detected anomalies from prior executions of the target system or other target systems;
• delivering the target-system multimodal event data to an orchestrator;
• utilizing the orchestrator to manage a plurality of artificial-intelligence (AI) agents, wherein the orchestrator selects, based on the target-system multimodal event data, a subset of the AI agents to perform operations including:
• detecting an anomaly in the target-system multimodal event data,
• accessing the lifelong learning repository of the previously-stored multimodal data and the previously-stored detected anomalies,
• performing specialized tasks tailored to that AI agent regarding the detected anomaly based on the previously-stored multimodal data and the previously-stored detected anomalies in the lifelong learning repository to identify candidate root-causes of the detected anomalies, and
• delivering an outcome of the specialized tasks to the orchestrator;
• and
• at the orchestrator, determining a recommending resolution to the detected anomalies based on (i) the outcomes from the subset of AI agents and (ii) the previously-stored multimodal data, the previously stored detected anomalies, and previously-computed model weights in the lifelong learning repository.