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.
Habr AI: Instead of Complex Agent Pipelines, Developers Should Embrace Markdown, Git, and Session Memory
Habr published a manifesto against overloaded agent pipelines: the author argues that for complex AI development, sessions, roles, rules, an
opencode-policy Plugin Adds 309 Rules to Protect AI-Agents from Injections and Data Leaks
For the opencode environment, a deterministic filter with 309 regex rules has been proposed: it intercepts prompt injection and dangerous to
Habr AI Explained How to Build a Production Agent With Durable State, Steps, and Events
Google DeepMind and South Korea Launch Partnership for Scientific Breakthroughs with AI
China blocks Meta's $2 billion acquisition of AI startup Manus ahead of US-China summit
Google warns about attacks on corporate AI agents via web pages
OpenAI Embeddings and RL: How to Build an Agent with Long-Term Memory for Accurate Answers
The tutorial shows how to train an RL agent to select relevant records from long-term memory so that an LLM answers questions about saved fa
Russian companies are shifting to systematic security for AI agents instead of point checks
Russian businesses are increasingly deploying LLMs and coding agents, so their security should be built not through analyzing each new tool,