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.
Shapes: an app where AI characters become group chat participants on equal terms with people
Shapes adds AI characters directly to group chats — they see the entire conversation, react to other participants, and remember the conversa
Product Graph and Agent Memory: Why AI Doesn't Save Products Without Knowledge Structure
An analysis of Product Graph explains why even powerful AI agents are useless without shared product memory and how interconnected knowledge
Redpine raises €6.8M from NordicNinja for API of licensed data for AI agents
Raft shows how companies can evaluate AI agents before deploying in workflows
Veai showed how to test AI agent in JetBrains IDE without model dependency
Kakao Mobility Reveals Level 4 Autonomous Driving Roadmap and Physical AI Strategy
Machine Learning Mastery released a guide on context engineering for reliable AI agents
Machine Learning Mastery showed why AI agents more often fail due to poor context management than due to the model, and how to fix it throug
Salesforce shows the future without screens: AI agents lead to disposable interfaces
Salesforce and WorkOS describe a shift from static screens to interfaces that AI assembles on the fly for a specific task, advising companie