Making a data agent trustworthy with a semantic layer and auditable memory

Data agents rarely fail loudly. They return plausible numbers, query data they shouldn't, and leave no trail you can audit. In this hands-on session, we run one text-to-SQL agent over a realistic 33-table BigQuery warehouse twice: once with the usual list-tables-and-guess tooling, and once with graph-based context from two Neo4j Labs projects. Neocarta adds a semantic layer of governed metric definitions, business synonyms and sensitivity tags, checked before any query runs. Neo4j Agent Memory records each reasoning step and what it touched, so you can trace a planted prompt injection to its source and quarantine it. You'll leave with a public repo to rerun everything yourself.