DEVELOPMENT OF AN AI DRIVEN SYSTEM MONITOR AND PROCESS MANAGER FOR WINDOWS OS

In today's computing landscape, it is all about the proactive and intelligent solutions that extend beyond the reactive nature of a standard system monitoring solution. This project focuses on creating an AI-based System Monitor and Process Manager for Windows OS in the Rust programming language, with real-time intelligent analysis powered by Google's Gemini API. It overcomes the shortcomings of traditional monitoring solutions, including fixed thresholds, lack of elasticity for changing workloads, and the absence of human-readable insights, by combining Rust's performance and memory safety with cloud-based large language model (LLM) reasoning. Through the use of the sysinfo crate system metrics, such as CPU utilization, memory, disk I/O, and network traffic are collected and packaged into prompts sent to the Gemini 1.5 Flash API, which provides actionable, natural language summaries for each metric that are categorized by anomalies, optimizations, and priorities. High metric accuracy was shown through validation testing with the Windows Task Manager (1– 3% deviation, which is acceptable for real time monitoring). The AI-generated responses have always been relevant to the content, and 75-80% of them contained specific recommendations. The non-technical evaluators and technical evaluators both provided user feedback that showed a great preference for the use of the natural language interface system as opposed to traditional numerical dashboards. Although complete automation of the process was not achieved for security and complexity reasons, the system provides a good proof of concept for the feasibility of a hybrid architecture between a low-level native monitoring system and an AI-based semantic analysis system. The tool can be deployed as standalone tool without any dependency that requires a Windows executable file, which makes it easy to use for both individual and enterprise users. This work presents a novel methodology for proactive system performance management, especially important for resource-constrained systems, and serves as a stepping stone for future advancements such as predictive analytics, automated process control, and local LLM inference. Key components: System monitoring, Windows OS, Rust programming language, Artificial Intelligence, Gemini API, process management, anomaly detection, real-time analysis.