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.

Abstract: In an embodiment, a workflow automation computer system comprises one or more hardware processors; one or more network interfaces that are communicatively coupled to one or more internetworks and capable of network communication with a browser extension hosted on an agent computer, a relational database system, and a support ticket system; and one or more non-transitory computer-readable storage media coupled to the one or more hardware processors and storing one or more trained machine learning models having been trained to output predictions of actions of web-based applications based on input specifying a plurality of browser events from interactions with the web-based applications; and one or more sequences of instructions which, when executed using one or more processors, cause the one or more processors to execute receiving, from the browser extension, one or more browser event objects corresponding to user input signals arising from interactions of the agent computer with the web-based applications, ext