An agent given a snippet writes against the snippet. Virgo grounds your agents in the real history (the feature, the decision, the constraint that is actually there). They reason about your system rather than a plausible-looking version of it.
Before the agent writes, it retrieves how this team actually runs jobs: the pattern, the constraints, and the backfill that made them rules.
Whichever agent picks up the task asks the same graph and writes against the same conventions. The diff fits your system no matter which tool produced it.
The agent writes from the same retrieved context the engineer uses to review the diff.
A grounded agent proposes the change your conventions expect, with the constraint behind it cited. Review stays focused on the feature.
A capable model with no context produces something fluent and wrong. Virgo retrieves the connected history behind the task at query time, over MCP. The agent argues from what is true about your system.
Early evaluations reached 97% context retrieval accuracy, compared with about 68% for baseline RAG or hybrid search when an agent was grounded in the real system’s history at query time.
Built on Context retrieval → and Agent memory →