Agentic AI is becoming the leading use case in enterprise AI adoption, and their reliability now decides whether that adoption succeeds. But these agents don’t always fail in obvious ways: an agent can generate valid SQL, complete a query successfully and return figures that appear reasonable while still misinterpreting the original request.
Because enterprise AI agents cannot rely on evaluation alone, we propose a comprehensive operating model that guardrails reliability at every layer.