GraphRAG for on-call incident resolution: lessons from production deployment at Microsoft
On-call engineers responding to high-severity incidents needed to answer time-critical questions spanning multiple documents — incident reports, runbooks, architecture docs, and postmortems — but Vector RAG could retrieve individually relevant chunks without modeling or traversing the relationships among them.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Engineer asks during incident
During a high-severity incident, an on-call engineer asks a time-critical question requiring multi-document reasoning.
Tools used
GraphRAGBenchmarkQEDAzure ML JobsPostgreSQLpgvector
Outcome
The team successfully deployed GraphRAG as an additive capability alongside Vector RAG for incident resolution, and established practical operational guidelines: use GraphRAG selectively for relationship-heavy queries, be deliberate about graph scope, and treat builds as a managed service with cost models, monitoring, and repeatable evaluation.
What failed first
Moving GraphRAG from prototype to production exposed hard operational challenges: indexing cost spikes from LLM-heavy extraction stages, complex update management that can cause graph drift, multi-dimensional evaluation requirements, and infrastructure gaps that most GraphRAG libraries do not address.