Agentic Reinforcement Learning: A Technical Overview Agentic Reinforcement Learning (Agentic RL) trains large language models to act as autonomous, decision-making agents. Instead of aligning a model’s text with human preferences, it teaches the model to perform multi-step tasks, use tools, and improve its strategy by interacting with a changing environment. Put simply: preference-based RL teaches […]

Framework for Building an AI Desktop Automation Agent An AI desktop automation agent performs tasks on a computer by interpreting natural-language commands. Unlike a script, it infers intent, runs multi-step workflows, and reacts to what the environment returns. This framework covers the architecture, the loop, and — critically — why you build and test it […]

A technology-agnostic architecture for interactive, full-stack agent applications: the front-end UI, the back-end API server, and the agent core — three layers, why they’re separated, and how data flows between them.