Context is the other half of the story, and the context landscape is honestly a mess. Vendors are working hard to convince you that only their semantic modeling language can save you, while it is not entirely clear whether these are necessary or even sufficient. Whether you keep your business logic in a semantic layer like MetricFlow or Malloy, or just in plain Markdown, the goal is the same: get that logic into a form an LLM can use. Context is almost always created by hand, and like all hand-written documentation, it starts drifting the moment it gets written down.
This highlights an opportunity, namely that agents are good at precisely the parts of context that are mechanical and bad at precisely the parts that aren’t. An agent can infer which tables join to which, what values a column tends to hold, what your sales regions are, and which tables people actually query. What it can’t infer is the stuff that was never really a data question: the right way to calculate revenue, what counts as a “customer,” when the fiscal year starts. Those aren’t facts hiding in the warehouse waiting to be found. They’re decisions, often business ones, that a person has to make. What an agent can do is flag the moment one of them quietly stops being true.
Automated agent insights remain a fantasy
The flashier pitch, where agents surface insights you never asked for, is the one I’d bet on last. It sounds wonderful to have hands-free analytics. An agent will keep watch over your data, notice what matters, and drop a dashboard tailored to whatever is happening today. But the bar is high for relevance and false positives can make human users lose confidence.
