Enterprises rushing to deploy autonomous data agents across thousands of disparate tables are colliding with a fundamental architectural limitation: brittle text-to-SQL generation and unchecked model hallucinations. While data leaders broadly agree that an enterprise ontology is non-negotiable before deploying agents into production, treating that ontology merely as prompt fodder to generate relational SQL misses the mark. An enterprise ontology is inherently a graph—a web of semantic definitions, business rules, and entity relationships. Forcing probabilistic large language models to continually translate intricate graph logic into rigid, error-prone SQL queries introduces friction, latency, and unnecessary execution failures right where accuracy matters most.
In this episode of The Digital Analyst, I sit down with Weimo Liu, CEO and Co-Founder of PuppyGraph, to explore a first-principles solution: querying the ontology directly as a graph through an ontology enforcement engine. By shifting away from traditional anti-fraud and cybersecurity graph silos toward agentic data architectures, Weimo details how modern graph engines serve as an active schema guardrail for AI. Instead of hoping an LLM gets the query syntax right, the enforcement engine audits each request directly against the graph schema prior to execution, rejecting misaligned queries with precise error feedback so the model can self-correct and retry. The result is a robust, zero-ETL approach that replaces fragile pipeline mechanics with dependable, production-grade intelligence.