Nobody deletes anything from SharePoint or from Google Drive, Confluence, Databricks volumes, or anywhere else where company knowledge lives. So when an agent goes to answer a question, it has to sort through everything: duplicates, contradictions, anything unfinished or missing a signature, and buried customer information that shouldn’t reach the model. The people building those agents have their own problem. They need a repeatable way to get all of that unstructured data agent-ready that doesn’t start from scratch for every new agent. Reece Griffiths, co-founder of Deasy Labs by Collibra, shows how customers analyze millions of documents across these systems before letting agents near them, and what happens to accuracy and token spend once agents stop reading the drafts. Key takeaways: → Find where wrong answers originate: conflicting versions, duplicates, unsigned drafts, stale files, PII → Give agents the metadata to pick the right file every time → Design how you’ll curate context once, then reuse it across every project