Commentary

Autonomous AI systems force architects into an uncomfortable question that cannot be avoided much longer: Does every decision need to be governed synchronously to be safe? At first glance, the answer appears obvious. If AI systems reason, retrieve information, and act autonomously, then surely every step should pass through a control plane to ensure correctness, […]

Our previous article framed the Model Context Protocol (MCP) as the toolbox that provides AI agents tools and Agent Skills as materials that teach AI agents how to complete tasks. This is different from pre- or posttraining, which determine a model’s general behavior and expertise. Agent Skills do not “train” agents. They soft-fork agent behavior

Our previous article framed the Model Context Protocol (MCP) as the toolbox that provides AI agents tools and Agent Skills as materials that teach AI agents how to complete tasks. This is different from pre- or posttraining, which determine a model’s general behavior and expertise. Agent Skills do not “train” agents. They soft-fork agent behavior

This is the first article in a series on agentic engineering and AI-driven development. Look for the next article on March 19 on O’Reilly Radar. There’s been a lot of hype about AI and software development, and it comes in two flavors. One says, “We’re all doomed, that tools like Claude Code will make software

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