Long-horizon agents — those capable of working through multi-step tasks over extended sessions — are no longer a research prototype. They are operational in real engineering environments today. But operational does not mean autonomous. The meaningful breakthrough of early 2026 is not that agents can now be left unsupervised; it is that

Vibe coding's most common real-world use case — "build me a website" — has had no rigorous benchmark until now. Vision2Web, accepted at ICML and built by Zehai He et al. at Zhipu AI, fills that gap with 193 tasks, 918 prototype images, and 1,255 test cases

JetBrains just announced Central, an open platform for unified AI agent management — and the timing reflects a specific operational crisis that's only visible once a team gets past the "our first agent works" milestone. The problem isn't agent capability. It's what happens

Most teams running web-augmented coding agents — those that issue live search queries before generating code — have no systematic defense against bad search results quietly degrading output quality. A new paper from Guoqing Wang et al. documents exactly what happens when that defense is absent, and introduces Sherlock, an automated pipeline

Every team running AI coding assistants hits the same wall within a few weeks: the AI generates code that misses the architecture, ignores testing standards, and breaks the deployment pipeline. A new breakdown of the rapidly-growing context-engineering-intro repository by coleam00 argues that the fix isn't a better prompt