TEXA OS: Self Improving Agentic AI and Safe Task Automation

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Chinese explanation / 中文解读

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Original abstract

Conversational AI assistants such as Siri, Google Assistant, and ChatGPT have improved how users interact with digital systems, yet they remain confined to answering questions rather than completing real-world tasks. Users must still manually open applications, navigate websites, fill out forms, and switch between software to finish even simple workflows, which limits productivity and creates accessibility barriers for elderly users, first-time computer users, and individuals with disabilities. This paper presents TEXA OS (Trusted Executive Assistant), a self-improving agentic artificial intelligence platform designed to function as a complete digital executive assistant rather than a conversational chatbot. Unlike static automation scripts built with tools such as Selenium or Playwright alone, TEXA OS combines natural-language intent understanding, autonomous task planning, and multi-agent execution to decompose a single spoken or typed request into an ordered set of executable subtasks. A FastAPI-based AI orchestrator interprets user intent using a large language model and delegates the resulting subtasks to specialized execution agents responsible for browser automation, operating system control, document generation, and communication automation. The system is built on a React and TypeScript frontend with continuous voice-recognition support through the Web Speech API and a PostgreSQL backend that persists user preferences, task history, and long-term AI memory, allowing the platform to adapt future task execution based on prior interactions. A distinguishing capability of TEXA OS is its Website AI Navigation Module, which allows users to retrieve information from complex websites—government portals, banking systems, and educational. The system was evaluated across unit, integration, and system-level test cases spanning voice recognition, browser automation, document generation, and permission-gated execution, achieving an overall task-execution success rate of approximately 96% and an overall system reliability of approximately 86%. The results indicate that agentic orchestration combined with permission-based safety controls provides a practical foundation for autonomous, trustworthy task automation across operating systems and web environments. Keywords: Agentic AI, Task Automation, Large Language Models, Browser Automation, Voice Recognition, AI Orchestrator, Natural Language Processing, FastAPI, Self-Improving Systems, Human-Computer Interaction.

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