AI Agents
AI agents are LLM-powered systems that don't just answer — they plan steps, call tools and drive a task to completion: writing code, searching the web, booking, analyzing. 2026 became the year of agents, from coding agents to autonomous researchers. This page gathers all our coverage of agentic AI: launches, protocols (MCP, A2A), reliability and real-world use.
OpenAI explained how to configure Codex: personalization, level of detail, and access permissions
OpenAI released a guide to Codex settings: where to enable personalization, how to choose the level of detail for its work, and why, for lon
OpenAI released a guide to Codex: how to set up the workspace, projects, and task flows
OpenAI published a practical guide to Codex explaining how to create a workspace, link a project to a folder, manage threads, and handle mul
AI startups in 2026 shift from a single prompt to multi-agent pipelines
Elon Musk revealed how the Macrohard project will unite xAI and Tesla to create software
Perplexity unveiled Personal Computer — an AI agent for Mac mini and teams
Habr AI showed how to prepare structured input for an AI agent instead of a raw technical spec
Claude Code and Codex compared on a real-world task: Claude is stronger in RAG, Codex saves tokens
A broad comparison of Claude Code and Codex shows that choosing a coding agent depends not on demo speed, but on task type, output quality,
Veai 5.6 for JetBrains IDEs adds commit message generation and manual Skills launch
Veai has released version 5.6 for JetBrains IDEs: the agent now suggests a commit message from the diff, offers quick actions in chat, suppo