Governance debt arises when data context, like column meanings or PII status, gets lost as data assets are transformed and shared. Traditional governance tools are reactive, often leading to delays in updating descriptions and documentation. The Governance Agent project aims to shift this paradigm by proactively propagating existing metadata downstream. It leverages column-level lineage to trace data origins and transfer descriptions, business glossary terms, policy tags, and trust scores. This automation reduces the burden of manual documentation, allowing data stewards to focus on higher-value judgment calls. The agent can also ingest context from external documents when direct lineage is unavailable. It uses a conservative approach, only applying metadata when there is strong evidence. Beyond lineage, it utilizes AI-driven scans to infer relationships and meanings when lineage data is incomplete. The project offers both a user-friendly dashboard for ad-hoc reviews and a CLI for automated pipeline integration. While powerful, the system requires human oversight for critical decisions and final approval. The goal is to redirect effort from repetitive tasks to valuable decision-making, improving data discoverability and accelerating AI solution development.