AI engineering lead
Owns the agent that guesses when the catalog is thin.
ACCURACY · METADATA GAPS
Empty column descriptions, and an agent that hallucinates to fill the gap
Thin catalog metadata leads the agent to invent structure it cannot see. Grounding in a governed model closes the gap the descriptions left open.
THE PROBLEM
When table and column descriptions in the catalog are missing or unclear, the agent fills the gap with guesses, so thin metadata turns directly into hallucinated answers.
THE PROBLEM ANATOMY
Who it hits
Data governance owner
Owns the catalog metadata the agent depends on.
Analytics engineer
Owns the tables whose names and comments decide the outcome.
The situation
A team enables an agent over tables with sparse metadata: terse column names, empty descriptions, no clear definitions. With nothing to anchor on, the agent infers what the columns mean, and the inference is often wrong.
The fix on offer is to document the catalog thoroughly and keep it current, which is real work that competes with everything else. Until it is done, every gap in the metadata is a chance for a confident wrong answer.
The specific challenges
Gaps become guesses
missing descriptions push the agent to infer meaning
Terse names mislead
unclear column names steer the agent wrong
A curation treadmill
accuracy depends on metadata that is never fully complete
Silent failure
a guessed meaning produces an answer that looks right
WHAT IT COSTS
PRACTICAL
Documentation debt
teams chase catalog coverage to hold accuracy
Answer auditing
outputs get checked against the real schema by hand
BUSINESS
Wrong decisions
a hallucinated field feeds a real report
Slow trust
the agent cannot be opened up until the catalog catches up
EMOTIONAL
Curation fatigue
the metadata backlog never feels finished
Second-guessing
analysts distrust answers over any thinly documented area
THE WAY OUT
A governed query engine and semantic layer for agent analytics
Swap in a pilot of e6data's query engine alongside the existing agent stack to:
Ground in the model
queries resolve against a governed semantic layer with defined entities and metrics, so answers rest on real definitions instead of inferred ones. The agent and open tables stay in place. What changes is that a metadata gap no longer becomes a guess.
Contain the blast radius
with a governed layer between the question and the tables, an answer from an undocumented column now fails visibly, rather than silently.
Keep the stack
the tables, catalog, and access controls stay as they are, and adoption is an endpoint change rather than a migration, needing only fast validation of the required integration mapping. What is added is a grounded path that does not depend on perfect metadata.
| Governed | definitions under each answer |
|---|---|
| Grounded | no inferred column meanings |
| 0 | data movement |
The result is answers grounded in real definitions, so a thin catalog no longer turns into a confident guess. Setup and connection details are in the query engine documentation.
TALK TO US
Book a demo on your own workloads
Reach out to book a demo, share challenges you’re facing, and tell us how this fits into what you’re currently working on or thinking about.
Problems we’re solvingThanks.
An actual person replies, usually within one working day.