Knowledge graphs for software delivery: an architectural approach | Harness Blog

Software delivery knowledge graphs address fragmented engineering data by representing services, builds, artifacts, deployments, security scans, infrastructure, and other SDLC entities through shared definitions and explicit, typed relationships. The proposed architecture combines version-controlled schema-as-code contracts, a unified synchronization layer that validates and links source data, and declarative queries that allow people and AI systems to retrieve consistent cross-domain answers without manually stitching together multiple tools. Its reliability depends on shared semantics, data contracts, completeness and freshness monitoring, provenance metadata, and automated governance for schema changes. The authors identify practical challenges including reconciling inconsistent entity names across platforms, coordinating changes among independent domains, detecting silent data degradation, and evaluating whether graph-based retrieval improves AI task success. When operated with application-level observability, testing, and continuous evaluation, such graphs can support real-time DORA metrics, faster vulnerability response, root-cause analysis, and cloud cost attribution, with the recommended approach being to begin with a few high-value relationships and expand gradually.

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