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When we launched our Governance Gap series, we set out to explore how the explosion of AI-assisted engineering changes the risk profile for modern software organizations. We looked at the rise of The New Software Creator and analyzed why deployment governance is what keeps teams safe when code production accelerates. We also mapped out the realities of security at scale and defined who owns governance accountability.

When we introduced the Low Code Plugin (LCP) framework in February, the premise was simple: if a system has an API, you should be able to build a plugin for it — quickly, with minimal code, and in a way you can share with the community. The "AI-ready" part was deliberate. The framework was designed to work naturally with AI assistants, so the path from idea to working integration would be as short as possible. That design decision is now paying off.

Anthropic, the company behind Claude, ran the most honest experiment in agentic AI: they gave a version of their own model $1,000, a small fridge in the office lunchroom, and one job. Run a shop. Make a profit. They named it Claudius. Claudius did not make a profit.

Most build-versus-buy debates assume you're choosing once, at the start, and living with it. Agentic engineering doesn't work that way. The decision shows up at every layer of the stack, and the teams getting it right aren't the ones who picked "build" or "buy" as a philosophy. They're the ones who know which layer is which.

How context engineering creates production-ready agentic AI What if the AI strategy you spent the past year building is already being measured by a completely different set of rules? I recently joined Amy Machado, senior research manager at IDC and Jim Malone, senior contributing editor at CIO Marketing Services, for a webinar where we explored how buyer expectations, architectural requirements, and evaluation criteria are shifting as enterprises move from search-driven experiences to agentic AI.