A practical guide to the four strategies of agentic adaptation, from "plug-and-play" components to full model retraining.

For much of the last two years, multi-agent systems have been treated as the natural next step in artificial intelligence. If one large language model can reason, plan, and act, then several working ...

The deal arrives as Meta accelerates its AI investments to compete with Google, Microsoft, and OpenAI — and as the industry’s ...

interview AI agents represent the new insider threat to companies in 2026, according to Palo Alto Networks Chief Security ...

AI agents will reshape 2026: they’ll feed on synthetic/structured data, remake the web, swarm unpredictably, and empower ...

Researchers explored the nuanced dynamics of how people balance their desire to speak out vs their fear of punishment in a ...

Step aside, LLMs. The next big step for AI is learning, reconstructing and simulating the dynamics of the real world.

Legacy metrics—uptime, latency, MTTR—no longer capture operational value in an AI-driven world. Mean time to prevention (MTTP ...

For IT and HR teams, SLMs can reduce the burden of repetitive tasks by automating ticket handling, routing, and approvals, ...

AI agents have emerged from the lab, bringing promise and peril. A Carnegie Mellon University researcher explains what's ...

In 2026, here's what you can expect from the AI industry: new architectures, smaller models, world models, reliable agents, ...