AgentLensGov

Data platforms increasingly receive SQL produced by agents alongside analyst and application traffic. AgentLensGov creates an identity-aware query ledger, attributes cost and policy exposure and flags join, scope and access patterns that diverge from approved models or reviewed query history. The input's phrase 'JOIN hallucination detection' is too absolute. A parser can identify missing relationships, risky fan-out or divergence, but it cannot prove business semantics or correctness. Human-versus-agent origin is reliable only when strong identity and routing evidence exist; otherwise it remains unknown. Historical queries are useful grounding but can contain legacy mistakes, excessive permissions and obsolete models. A conformance score measures agreement with explicit rules and validated examples, not compliance, data quality or truth. Identity assertion, authorization, query proposal, policy decision, warehouse acknowledgement, result metadata, reviewer disposition, downstream use and business outcome remain separate. The product can improve visibility and reduce preventable query risks. It cannot guarantee correct analytics, complete attribution or regulatory compliance.

A data-platform owner operating shared warehouse access for analysts, applications and AI agents with cost, security and semantic governance responsibilities.