Building a trustworthy agent meant verifying every column, and the verification surfaced data problems the client did not know it had.
An agent that trusts the data blindly repeats every defect in it, confidently. Wrong currency handling becomes wrong totals. Conflicting table conventions become answers that quietly disagree with each other.
Before the agent could be trusted, the data had to earn that trust.
A probe first method. Every table the agent uses is interrogated with real SQL before a single rule is written. Blanks, ranges, conventions, joins.
Every anomaly becomes one of two things. A written rule the agent obeys, or a finding handed to the client with the evidence behind it.
Each finding is now either a written rule the agent obeys or an entry in a data quality register with a cleanup owner.
The rules keep the agent's answers honest. The register gives the client a worked list of defects to fix at the source.
The client got a data audit as a by-product of getting an AI agent.
Not sure your data can carry an AI agent? We verify every column first and hand you the findings.