Reviewing AI-generated context in DataHub's Context Hub

AI agents can auto-generate business context, but someone still needs to review it before it's applied. This Short shows how DataHub's Context Hub puts a human checkpoint between agent-generated context and the systems that act on them. Manuela Wei, Principal Product Manager at DataHub, walks through the reviewer experience: a task center inbox where data engineers, analysts, or business experts see the documents an agent has produced — flagged as unapplied until a human signs off. Each item shows the question being answered, the tables used to query it, and the calculations behind it. Reviewers can edit definitions directly, leave comments for collaborators, and run evals against golden queries to confirm the context holds up. They can also simulate how a proposed change would affect an answer from Ask DataHub, DataHub's native AI agent, before applying it. This clip covers the core review workflow inside Context Hub: inline editing, reviewer comments, eval checks, and answer simulation — the guardrails that keep auto-generated business semantics accurate before agents rely on them. Featuring: Manuela Wei, Principal Product Manager, DataHub This is a clip from DataHub's June 2026 Town Hall. Watch the full session on DataHub's YouTube channel for more on the Context Platform roadmap. 🔗 Learn more about the DataHub Context Platform: https://datahub.com/products/context-platform/ 🔗 Full June 2026 Town Hall: https://www.youtube.com/watch?v=JboXwGIz54k 🔗 DataHub Context Management Learning Center (40+ free resources on context management): [https://datahub.com/learn/context-management/ #DataHub #Context #AIAgents #ContextManagement #MetadataManagement