Metadata Grounding Stops One Word Too Early

"How are Mac sales in NA?"

Six words, zero table names, zero column names.

Every demo you have watched would have used a question shaped like a schema. It probably included column descriptions, catalog entries, or governed metric definitions.

In other words, everything about the containers, and nothing about what sits inside them. The real world works differently. Every real user asks a question shaped like a sentence, and the gap between those two shapes is where production accuracy falters.

What metadata grounding is, and why everyone is right to do it

Metadata grounding involves feeding the model a description of your data before it writes a query.

Feeding the model your schema, column descriptions, catalog entries, and governed metric definitions is correct and necessary work. It is what a semantic layer publishes and what OSI (Open Semantic Interchange) standardizes.

Agents need this to function. An agent without your metric definitions will invent one, and an invented definition of net revenue is how a CDO ends up explaining a variance to the board. Every text-to-SQL and agentic analytics product grounds on metadata, and is the reason the demo from before worked at all.

But real users don’t ask questions in metadata. They type "how are Mac sales in NA," which contains two values and no column names. The agent has to guess which column holds the value and how that value is stored.

Your agent knows the container, not its contents.

Both sides of the WHERE clause are unknown

To answer the question mentioned earlier, the agent needs to know which column holds "Mac" and how your data stores "NA."

- Is it North America, NA, or region code 03?

- Does NA mean North America, Not Available, or Namibia?

- Is Mac a product family, a product line, or a value that appears in three columns with different meanings?

Metadata doesn’t answer these questions. All it does is describe the column. Now, your column description might say "region." It doesn’t specify whether that region is stored as a two-digit code, or that 03 is North America, or that your CRM export uses NA to mean something else entirely.

So the agent guesses and picks the most plausible string, writes clean SQL, and hands back an output. More often than not, that guess is wrong.

Which raises the obvious question. If values matter this much, why did an entire category build up to the edge of the schema and stop there?

Why the industry stopped exactly at metadata

The category stopped at metadata for a physical reason.

Container words are finite, so a schema fits in a context window. However, content words aren’t, since forty million distinct customer names never will.

Vendors know this, and the honest ones document the boundary. Snowflake's Cortex Analyst will embed sample values directly in the semantic model for a dimension with roughly one to ten distinct values. Anything with higher cardinality requires you to stand up a separate search service and wire it in yourself.

Empty results are survivable, but confident wrong answers aren’t

A query that returns nothing is a self-reporting bug. The user sees zero rows, says "that's wrong," and files a ticket within the minute. Your team fixes the mapping and moves on.

But, a query that runs cleanly, returns rows, and silently filters on the wrong value could go into a board deck. Nothing in the output announces that the filter matched the wrong thing.

Six weeks later, someone reconciles against the ERP and the trust you spent a year building is gone in a single meeting. This trust gap is one of the top reasons why AI pilots fail once they’re deployed in production. Informatica's survey of global data leaders found that 67% could not move even half their GenAI pilots into production, and 38% cited lack of trust in data quality as a barrier to showing business value.

What WALT does before composing a single clause

Walt treats this as a first class search problem. We have a signal based search engine that deploys first to identify the best matching attribute and best value, because the same value could be in multiple attribute and data products.

WALT’s crew of autonomous data engineers build a semantic index over the values themselves. The index sits inside the Data Context Graph, alongside the ontology and the metric definitions. The agents keep the data context graph current. New values appear, old ones get retired, and a source system renames a code at 2 AM. The graph re-evaluates and surfaces the change as a tracked update.

None of this requires new infrastructure. WALT runs on the Snowflake, dbt, Tableau, and catalog you already own, everything it builds stays in your warehouse. No lift and shift. required.

Bottom line

Metadata grounding tells your agent which column holds the answer. Something still has to tell it which value to match. See how WALT works, or book a demo to see how WALT’s crew of autonomous specialists build and maintain this ecosystem for you.

Sources

Snowflake, Salesforce, dbt Labs, and More, Revolutionize Data Readiness for AI with Open Semantic Interchange Initiative | Snowflake

Improve literal search to enhance Cortex Analyst responses | Snowflake Documentation

Global Data Leaders Seek to Harness the Power of GenAI for AI-Driven Success | Informatica

The autonomous data engineer handbook | WALT