In the enterprise, a confidently wrong AI agent answer can come from something more basic than the model: the agent doesn't know what the business means by its own terms, like what counts as revenue or which customer record is current.
The VB Pulse tracker has followed how enterprises are responding to that across three separate waves since June. Across all three waves, most respondents report running, piloting or building a governed semantic layer: 58% in June, 63% in July and 67% in August. But far fewer name that layer as their agents’ primary source of business context: 21%, 19% and 13%, respectively.
Context failures remain widespread. Sixty-four percent of August respondents traced a confidently wrong agent answer to missing or inconsistent business context in the past six months. July was 68%, and June was 57%.
Agents get context somewhere else
Respondents named several different primary sources of business context for their agents. Retrieval over documents was named by 32%. Direct queries to live systems, where an agent runs SQL or calls an API or an MCP server and reads whatever comes back, were named by 21%, up from 11% in July, a change large enough to call. The gap between retrieval and direct queries is too close to call. Long-context loading, pasting a large set of documents or records straight into the model's context window, was named by 19%. The governed semantic layer was named by 13%.
Neither direct queries nor long-context loading carries a governed business definition by default. A direct query can return whatever sits in the table without telling the agent what the column means. Long-context loading hands the model raw material without necessarily giving it an agreed definition of what the business means by its own terms. Either approach can be paired with a governed layer; the survey does not tell us whether respondents are doing that.
Enterprises adopting a governed layer report more context failures
Enterprises running, piloting or building a semantic layer report a confidently wrong agent answer far more often than enterprises still evaluating one or with no plans at all, 78% against 37%. A July wave of a separate 101 respondents found the same pattern, 89% against 35%. The gap holds even after setting aside enterprises that don't run agents on their own data or don't track root cause: 79% against 41% in August, 90% against 46% in July.
VB Pulse did not ask respondents why the two groups differ. The August report offers two possible explanations. One is that a semantic layer gives a team a correct definition to check a wrong answer against, so these enterprises catch more of their own failures rather than producing more of them. The other is reverse causation: enterprises that already had context failures may be the ones that went on to build a semantic layer, rather than the layer causing the gap. The survey cannot tell which explanation is driving the gap — or whether the layer itself changes the underlying failure rate.
How vendors are approaching the gap differently
Some vendors moving toward direct queries are putting context around the query path in different ways. OpenAI's Data agent, launched inside ChatGPT Work in September, connects directly to warehouses including Snowflake, Databricks and BigQuery and pulls context from Slack, BI dashboards and file storage in the same pass. Asked whether the agent builds a persistent context layer across a customer's tools, Arpan Shah, general manager of the enterprise technology vertical at OpenAI, told VentureBeat it does not.
"It combines all the various contexts in their state, as opposed to generating a net new context layer itself," Shah said.
Keewano blurs that distinction further. Its KeewanoDB, also launched in September, lets agents read raw event sequences directly through MCP, the kind of direct query the survey is measuring. But Mark Kardashov, co-founder and CEO of Keewano, described a semantic layer built during ingestion, using large language models, that attaches additional context to each event as it lands. A direct query against KeewanoDB is not necessarily a query with no semantic context behind it.
Snowflake is approaching the same gap from the routing side. The company's Cortex AI Gateway, which auto-routes a task to a smaller model first and escalates only when needed, depends on the task arriving with context already prepared. Baris Gultekin, Snowflake's vice president of AI, explained in August that without good context, a model has to do the exploratory work itself, writing and testing SQL, searching through data and retrying when something doesn't work, a process that's expensive and typically needs a more capable model. Packaging the context in advance removes that step. Snowflake says its Horizon Context and Cortex Sense tools provide context capabilities for that preparation before the model has to explore the data itself, while Snowflake's existing access controls govern the data and models the agent can use.
"The interesting part is what it says about where differentiation has moved," Sanjeev Mohan, principal at SanjMo, told VentureBeat in August. "Snowflake isn't really selling routing, it's selling routing that never leaves the governed data boundary, with access controls, tagging and cost attribution already attached."
What this means for enterprises
Building a semantic layer does not decide what an agent reads. Two-thirds of August respondents said their organizations run, pilot or build one. Even among that group, only 20% name the semantic layer as their agents’ primary source of business context.
Whether a direct query carries business context behind it depends on what sits behind it. Keewano says it attaches context at ingestion, Snowflake pairs context preparation with its existing access controls, and OpenAI's Data agent assembles context on the fly without building a persistent layer.