AI agents should retrieve facts, not define them
Executive Take
Unsupervised AI agents that pull straight from databases will quietly make business decisions no one approved. Fix this by building a semantic layer that encodes your definitions once, so the agent retrieves facts instead of inventing them.
Executive Summary
A CIO.com column argues AI agents fail when they query raw databases or APIs directly, because conflicting or stale data forces the agent to guess at answers it has no authority to decide. In one test, LLM accuracy on applying business rules dropped from 97% at 5 rules to 43% at 50 rules across nine models. The fix: route agents through a semantic layer or resolution layer with pre-agreed definitions, not direct database queries.
Why It Matters
AI and technology leaders rolling out customer-facing or board-facing AI agents risk real credibility and trust damage if the agent's numbers can't be audited or trusted. This is a practical governance fix, not just a technical nuance.
Bizquad Perspective
Most leaders are chasing bigger models to fix accuracy, but this piece shows the real fix is organizational: someone with authority must resolve data conflicts before the agent ever sees them.