An enterprise reasoning graph connects the tickets, threads, docs, meetings, code changes, and AI workflows behind your systems. It links each artifact to the decisions, constraints, people, and downstream work around it. Engineers and AI agents use the graph to trace how work happened.
Search indexes artifacts. A reasoning graph links decisions, constraints, artifacts, and provenance into structured engineering context.
People, projects, decisions, constraints, discussions, and code changes become nodes. The same entity mentioned across tools maps to one node.
Edges carry meaning: triggered, constrained, decided, replaced. Queries follow a symptom or change to its cause across tickets, threads, docs, meetings, and code.
Each node keeps its source, author, and timestamp. Answers cite the exact artifacts behind each claim.
Virgo checks the access controls of connected tools at query time. Engineers and their agents retrieve the reasoning they already have permission to see.
Search, RAG, and assistant memory each handle one slice of context. A reasoning graph connects the decision context across tools.
A knowledge graph catalogs what exists and how entities relate. A reasoning graph records the decision behind a change, the constraint that shaped it, and the alternative that was ruled out. Queries return the decision lineage behind an implementation.
Vector search and typical RAG retrieve artifacts that match the prompt. Engineering reasoning often sits across artifacts with different wording: a meeting note, Slack thread, Jira ticket, and pull request. A reasoning graph follows typed edges across that chain.
Assistant memory stores what one agent learned in earlier sessions. A reasoning graph grounds agents in shared engineering history with provenance and permissions. MCP clients read from the graph and write useful learnings back.
The same graph supports context retrieval, decision lineage, agent memory, and knowledge retention.
Retrieve the connected work history behind any feature, bug, or change across the tools it touched.
See context retrieval →Trace a rule in the code back to the decision, the constraint, and the alternatives behind it.
See decision lineage →Capture useful AI-agent learnings as reusable context for future agents, developer tools, and workflows.
See agent memory →Preserve the reasoning behind your systems when the engineers who built them move teams or leave.
See knowledge retention →Short answers to the most common questions.
An enterprise reasoning graph turns engineering work into connected entities and causal edges. People, decisions, constraints, artifacts, and code changes become nodes. Edges show what triggered, shaped, or followed each decision. Engineers and AI agents query the graph to understand why systems were built this way.
A knowledge graph records what exists and how entities relate. A reasoning graph records the triggers, constraints, decisions, and ruled-out alternatives behind the system. Queries reconstruct how the system came to be.
Typical RAG retrieves artifacts that match a prompt. A reasoning graph follows causal links between artifacts with different wording and returns the connected trail with provenance.
Agents reach Virgo through the Model Context Protocol. Before a task, they request scoped context, follow prior decisions, and write new learnings back as provenance-backed graph entries. Future agents retrieve those entries when permissions allow.
Virgo connects to the tools your team already uses and captures work as it happens. During ingestion, it extracts entities, resolves duplicates, and links them across tickets, threads, docs, meetings, code, and AI workflows.