AI agents in practice: how they work, which workflows hold up in real teams, and what fails quietly, from MCP to observability and multi-agent failure rates.
Agentic AI isn't a new species of AI — it's a generative model wired into a loop that lets it plan, use tools, and act toward a goal with minimal prompting. Here's what that loop actually looks like, how agents really connect to tools, and where the genuine risks sit.
★ FeaturedThe old "top 10 AI tools" list ages fast — several of the tools on the original version have since renamed, repositioned, or been retired outright. Here's what's actually current in mid-to-late 2026, and what each tool is genuinely good for.
Open-weight models trail the closed frontier by about four months, yet enterprise use of them fell from 19% to 11%. The gap that now matters is trust, not benchmark points.
AI agents fail silently, returning confident, wrong answers with no error thrown. Here's how teams are building observability to actually catch it.
Research shows "model collapse" is real under one training pattern and avoidable under another — the difference comes down to one design choice.
Gemma 4 jumped from 20.8% to 89.2% on AIME math at similar size — new research shows exactly when distillation works, and when it doesn't.
Nearly every tool on the standard "workflow automation" list has rebuilt itself around autonomous AI agents in the past year — including one, formerly Intercom, that's now being acquired by Salesforce. Here's what's actually changed and what still just works.
Startups don't need a data team to get real value from their data anymore. Here's what AI actually does differently in today's visualization tools — and which real, current tools to look at instead of vague "AI-powered dashboard" marketing.
Three real, documented AI failures from 2025 — a hiring-bias lawsuit, a self-driving car recall, and a support bot that invented a fake policy — and what each one actually teaches about deploying AI in production.
