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), so they reason about your system instead of a plausible-looking version of it.
Before the agent writes, it retrieves how this team actually runs jobs: the pattern, the constraints, and the pileup that made them rules.
Whichever agent picks up the task asks the same graph and writes against the same conventions, so the diff fits your system no matter which tool produced it.
The same retrieved context serves the engineer reviewing the diff and the agent that wrote it.
A grounded agent proposes the change your conventions expect, with the constraint behind it cited, so review is about the feature instead of about the guesswork.
A capable model with no context produces something fluent and wrong. Virgo retrieves the connected history behind the task at query time, over MCP, so the agent argues from what is true about your system.
Up to 97% in early evaluations, versus about 68% for baseline RAG or hybrid search.
Built on Context retrieval → and Agent memory →