DataHub, provider of a popular open-source metadata and context platform, is releasing DataHub Cloud Context, offering new capabilities that turn context management from a one-time setup into a system that continuously proves and improves agent accuracy.
According to the company, DataHub Cloud Context now ships with three new capabilities that form a sequenced approach that delivered uniformly consistent, trustworthy agents in the five-month beta that preceded general availability.
New capabilities and features include:
- Domain-scoped Context Management lets administrators group data assets, context documents, and eval questions into focused domains, such as sales reporting or product analytics, and connect agents to only the domains they need. Teams start narrow to their own specialized domains, prove accuracy to stakeholders within weeks, and expand from a measured baseline rather than trying to document an entire enterprise data estate at once.
- Cloud Context Evals give teams a persistent accuracy harness. Administrators define test questions and expected answers for a given data domain, and the platform continuously monitors whether agent responses meet those criteria as underlying data and context change.
- Agent Feedback, through MCP, lets agents flag missing, invalid, or incorrect context based on real interactions with end users. It also provides a monitoring dashboard for data teams to proactively monitor and improve context, ensuring gaps are closed on ungrounded questions over time. When an agent encounters an ambiguous metric definition or a user corrects a result, that learning persists across agents, sessions, and platforms. Institutional knowledge compounds with every query instead of resetting between sessions.
Together, these three capabilities create a closed loop. Teams scope a domain, prove accuracy with evals, and let agents report feedback from real user conversations back into the context graph. Accuracy increases with use instead of decaying over time, the company said.
DataHub organizes context into three layers. The technical layer captures what exists across the data estate, from tables and columns to dashboards and pipelines. The semantics and metrics layer defines how tier-1 KPIs are calculated, so questions about critical metrics return consistent, correct answers regardless of who is asking, or which tool is doing the asking. The knowledge layer explains why those assets and definitions exist and how teams should apply them to business outcomes, the vendor said.
"The clearest learning from the beta is that you have to start narrow and prove accuracy before you expand," said Shirshanka Das, co-founder and CTO of DataHub. "Customers who identified a specific data domain, set up eval questions with their subject-matter experts, and then layered in DataHub context saw clear, measurable accuracy gains. That sequence is now built into the product as a guided workflow."
DataHub Cloud Context is generally available now.
For more information about this news, visit www.datahub.com.