A2UI (Agent-to-User Interface) is Google’s open protocol that lets an AI agent describe an interactive interface as secure, declarative JSON, which the client renders with its own native components.

AI-powered automation tooling splits into two families, and choosing the wrong one wastes weeks. Agentic orchestration platforms (e.g., OpenAI AgentKit) build agents that reason, plan, and adapt. Deterministic automation platforms (e.g., n8n, Make) execute predefined, event-triggered sequences. This guide compares them and shows when each fits. The three platforms OpenAI AgentKit — OpenAI’s pro-code framework […]

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 an LLM what to say; Agentic […]

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 in a simulation before letting it touch a […]

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.