AI : Learned Execution Graphs for Real-Time Anomaly Detection & Drift Classification in APIs#

Speakers: Ritvik Pandya (sponsor session)

• Why: ML for infrastructure operations is one of the PM's explicit interests, and this is a production system applying learned models to API middleware pipelines — with concrete, quantified results rather than AIOps generalities.
• What: Models the API ingress pipeline (auth, validation, rate limiting, routing, caching) as a DAG learned from structured telemetry at 1,600+ TPS, then applies it three ways: per-node anomaly attribution (in production this pinpointed a 41x deviation at a single node invisible to service-level monitoring, cutting root-cause time from 2–3 hours to under 30 seconds); structural drift detection (missing, reordered, or injected middleware nodes that latency/error monitors reported as "healthy"); and per-client graph fingerprinting that classifies behavior change vs. configuration drift vs. infrastructure failover via KL divergence. Also a novel retry-storm detection approach using idempotency-key entropy, breaking amplification loops by returning cached responses at the specific graph node (from abstract only).
• How: One structured event per processing step; per-endpoint execution graphs built via sequence mining with statistical confidence thresholds; per-node baselines (latency, dependency, execution frequency) and deviation ratios; exponential moving averages for client profiles.
• Where applicable: Any middleware/ingress pipeline that can emit structured per-step telemetry — broadly applicable. It is a sponsor slot, so expect product framing, but the graph-construction and attribution methodology is the transferable core.