Automatically determining workstreams from common actions in online applications

Abstract

An embodiment uses a computer-implemented approach to identify and automate workstreams from common actions in online applications. A browser extension collects and processes user event metadata, which is used to train machine learning models for workflow automation. The extension reads relevant fields from web services and delivers prompts with high-level actions (HLAs) based on user or organizational details. These HLAs are appended to prompts and sent to a large language model (LLM) via an API call. The LLM responds with a solution and a sequence of actions, which the browser extension executes automatically. Users retain control to pause, accelerate, or modify these actions. This method ensures secure, end-to-end workflow automation across various computing devices, enabling efficient and autonomous resolution of tasks while maintaining user oversight. The browser extension processes metadata, not enterprise data, ensuring security and privacy.