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Wyebot’s presentation at Mobility Field Day 14, delivered by CTO Anil Gupta, introduces the DEX (Digital Experience) Agent, a software-based solution designed to provide real-time telemetry from end-user devices. This product complements Wyebot’s existing hardware sensors, addressing the needs of a post-COVID hybrid workforce by monitoring connectivity both in the enterprise office and at home. The core of the DEX Agent is its Health Score, a composite metric designed to provide IT administrators with a single, glanceable indicator of device health. To ensure transparency for technical users, Gupta details the score’s internal logic, which is divided into four primary weighted categories: connectivity (25%), connection stability (40%), network tests (20%), and device performance (15%).
The summary continues by breaking down these categories, noting that connectivity covers initial establishment phases like DHCP and authentication, while stability monitors ongoing metrics such as signal strength and data rates. A key differentiator for Wyebot is its partnership with Intel Connectivity Analytics (ICA), which provides deep driver-level insights–such as missed beacons–that are typically inaccessible to standard operating systems. The agent also runs active network tests, including a native Microsoft Teams test that monitors audio, video, and screen-sharing streams to diagnose whether meeting issues stem from the network or application routing. Device performance monitoring adds context by tracking CPU and memory utilization, helping administrators identify if hardware thermal throttling or resource exhaustion is mimicking a poor Wi-Fi experience.
Looking toward future developments, Wyebot is expanding the DEX Agent to macOS and exploring mobile platforms like Android for warehouse use cases. The roadmap includes automated location detection, which uses AP BSSIDs to automatically categorize devices into specific offices without manual mapping. Gupta also emphasizes the platform’s commitment to simplicity and security through Agentic AI, utilizing a Model Context Protocol (MCP) server that allows enterprises to use their preferred LLMs without exposing sensitive data to public models. The presentation concludes by highlighting the flexibility of the system, which supports customizable application tests and continuous packet capture on sensor hardware, ensuring that both synthetic and real-user data are available for deep-dive troubleshooting.
Personnel: Anil Gupta
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