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.

Google explained the difference between Google-Agent and Googlebot for AI access and indexing
Google described how the new Google-Agent differs from Googlebot: the first performs actions on sites at user request, the second automatica

Why the Pentagon-Anthropic conflict became a warning sign for AI business
The dispute between the US Department of Defense and Anthropic over model usage restrictions revealed a new corporate risk: AI strategy depe

Habr AI: LLMs Can Take Over Routine in Business Research—But Not Strategy

Agent-Infra Introduces AIO Sandbox — Unified Environment for AI Agents with Browser and Shell

OpenClaw deployed on Wiren Board: how an AI agent controls the controller and writes scripts

Cursor releases TypeScript SDK for coding agents with cloud sandboxes and token-based pricing

Microsoft taught Copilot Researcher to cross-check GPT and Claude answers in a single process
Microsoft added a Critique mode to Copilot Researcher, where GPT prepares the answer and Claude verifies it for accuracy, while simultaneous

Raft Analyzed Where MCP and Thin MCP Add Latency to AI Agents
Raft compared five architecture options for AI agents and showed that the main cause of delays is often not Python or HTTP, but the number o













