An agent works out something hard (a migration order, a flaky-test cause, a non-obvious constraint). When the session closes, Virgo commits that learning to the graph. The next agent and engineer start from it rather than rediscovering it.
Behind one question, the flake’s whole world lights up: the test, the fixture, the CI config, and the two earlier reports nobody linked. The conclusion writes back to the graph.
Every agent commits what it learns to the same graph, with provenance. A finding from one session becomes context for the next, in any tool.
A committed learning carries into the next engineer’s work, the next agent’s run, and the next quarter.
A lookup replaces an afternoon of digging.
An agent that forgets everything at the end of each session can only ever be as good as its last prompt. One that writes learnings to the graph, with provenance, makes every solved problem context for the next one.
Roughly 3x fewer iterations in code generation when the last investigation’s finding is already committed to the graph.
Built on Agent memory → and Agents grounded in real context →