ix-understand

You can map software architecture by using graph-first evidence to build a structural mental model. This approach uses codebase scale metrics and subsystem ranks to generate an overview, avoiding broad source reading and wasted tokens.

Graph reasoning for system decomposition builds an architectural mental model by parsing graph data evidence. It runs orient commands in parallel, labels structural claims as graph or inferred, and applies confidence scoring to map subsystems accurately.

Speed up developer onboarding by generating a detailed architectural mental model of the repository. Using graph data and confidence-aware caveats provides a rapid understanding of structural shape, key subsystems, and components before deeper investigation.

Yes, you can control architectural analysis depth using shallow, medium, and deep modes. Medium and deep modes delegate subsystem exploration to agents, balancing speed versus completeness while reducing manual graph command work.

Agent orchestration supports risk-aware analysis by delegating deeper subsystem exploration during design reviews. It uses confidence scoring and labels claims as graph or inferred, ensuring structural understanding is backed by evidence before changes.

Graph data for codebase understanding relies on available metrics and subsystem ranks. Claims are labeled as graph or inferred with confidence scoring, meaning highly inferred architectural models may require deeper agent-backed exploration to validate structural assumptions.