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.
Introduces NLP, the AI discipline that lets machines read, interpret, and generate human language. Covers the processing pipeline, the NLU/NLG split, common applications, the arc from rules to transformers, and NLP’s role as the intent engine for agentic AI.
A guide to Oh My Claude Code (OMC), a plugin that turns the Claude Code terminal into a multi-agent orchestration system with specialized agents, five execution modes, and model routing.