AI & ML

In a previous article, we outlined why GPUs have become the architectural control point for enterprise AI. When accelerator capacity becomes the governing constraint, the cloud’s most comforting assumption—that you can scale on demand without thinking too far ahead—stops being true. That shift has an immediate operational consequence: capacity planning is back. Not the old […]

I’ve been telling myself and anyone who will listen that Agent Skills point toward a new kind of a future AI + human knowledge economy. It’s not just Skills, of course, it’s also things like Jesse Vincent’s Superpowers and Anthropic’s recently introduced Plugins for Claude Cowork. If you’ve never heard of Skills or Superpowers or

My first post made the case for what a semantic layer can bring to the modern enterprise: a single source of truth accessible to everyone who needs it—BI teams in Tableau and Power BI, Excel-loving analysts, application integrations via API, and the AI agents now proliferating across organizations—all pulling from the same governed, performant metric

Most multi-agent AI systems fail expensively before they fail quietly. The pattern is familiar to anyone who’s debugged one: Agent A completes a subtask and moves on. Agent B, with no visibility into A’s work, reexecutes the same operation with slightly different parameters. Agent C receives inconsistent results from both and confabulates a reconciliation. The

For most of the past decade, AI governance lived comfortably outside the systems it was meant to regulate. Policies were written. Reviews were conducted. Models were approved. Audits happened after the fact. As long as AI behaved like a tool—producing predictions or recommendations on demand—that separation mostly worked. That assumption is breaking down. As AI

For most of the past decade, AI governance lived comfortably outside the systems it was meant to regulate. Policies were written. Reviews were conducted. Models were approved. Audits happened after the fact. As long as AI behaved like a tool—producing predictions or recommendations on demand—that separation mostly worked. That assumption is breaking down. As AI

Agentic AI has clearly moved beyond buzzword status. McKinsey’s November 2025 survey shows that 62% of organizations are already experimenting with AI agents, and the top performers are pushing them into core workflows in the name of efficiency, growth, and innovation. However, this is also where things can get uncomfortable. Everyone in the field knows