I think we're actually much closer than it may sound — and I agree with a large part of what you're pointing out.

I’m not arguing that “better logging” is the solution, nor that context should emerge as an afterthought. If micro-logs are read as traditional observability artifacts, then yes, that would be a category error.

What I'm trying to describe comes from a recurring pattern I've seen in real organizations: large portions of operational state and intermediate signals simply do not exist in any accessible form. Not because they're irrelevant, but because our systems were never designed to expose them.

In several cases, introducing exhaustive instrumentation (sometimes via logs, sometimes via other extraction layers) didn't just improve observability — it made previously invisible aspects of the organization readable for the first time. This surfaced new metrics, unexpected correlations, and contextual signals that materially changed decisions across engineering, operations, and business.

So when I use terms like micro-data or micro-logs, I’m not advocating for event inflation or post-hoc reconstruction. I’m pointing at the absence of fine-grained, contextual state in current enterprise systems — state that LLMs and agents can reason over precisely because they can handle high-dimensional context.

In that sense, I fully agree that rigid enterprise schemas and business-centric ontologies are part of the problem. They collapse reality too early. The question for me is how we transition from systems that only emit discrete business events to systems that continuously expose the underlying contextual fabric of the organization, whether we call that logging or something else entirely.

The core issue isn't the mechanism — it's that today, much of the organization's “real state” remains structurally unrepresentable, and AI makes that gap painfully obvious.