Hound is a graph-based audit agent that improves system-level reasoning across interrelated components in complex codebases. Instead of relying on broad file chunks or language-specific tooling, Hound builds flexible, analyst-defined knowledge graphs (e.g., monetary/value flows, authentication/authorization roles, call graphs, invariants) with compact annotations. Investigations are planned in two phases: a Coverage sweep to quickly map components, then an Intuition/Saliency phase that targets high-impact, contradiction-rich leads. A persistent belief system tracks hypotheses with explicit evidence and confidence, while a QA Finalizer reviews high-confidence items over full source context to confirm or reject findings. On a five-project subset of ScaBench, Hound raises micro recall and F1 over a baseline LLM analyzer (recall 31.2% vs. 8.3%; F1 14.2% vs. 9.8%) at a modest precision trade-off typical of exploratory audits. Gains stem from relation-first graphs that enable exact, cross-component retrieval and a disciplined hypothesis lifecycle. The artifact includes code, graph builders, benchmark harnesses, and scripts to reproduce tables and HTML reports.