Modern cloud native systems often span hundreds of microservices, thousands of endpoints, and fragmented telemetry across logs, traces, metrics, deployments, and service catalogs. Even with strong observability, engineers still struggle to answer two high impact questions fast: why did this deployment fail, and if I change this service or API, what breaks.

This industry session presents an AI powered Microservice Cognitive Index, an intelligence layer on top of existing observability. It builds a canonical evidence graph by ingesting telemetry, deriving runtime topology from traces, clustering incident signatures from normalized logs, correlating regressions with deployments, and propagating change impact through dependency and contract signals. It combines graph based reasoning with machine learning and large language models to summarize evidence, rank likely causes, and explain blast radius with confidence. Unlike chat with logs approaches, it enforces tool grounded answers with evidence references, confidence scoring, and refusal policies when data is incomplete or confounded, making results auditable and safer for operations.