Every agent has a defined role, a bounded scope, and an explicit interface. No black boxes. No hidden prompts. No un-logged actions.
The oversight lifecycle runs as a directed graph. Every node is an agent class. Every edge is a conditional transition. Every state is logged.
Continuous KS-test drift monitoring, performance degradation detection, latency SLA alerting. Sub-10s detection latency across all live model outputs.
Root cause analysis with feature-level attribution. Isolates which input distribution shifted, by how much, and which tenants are affected.
Structured remediation plans with cost, risk, confidence, and estimated recovery time attached to every proposed action.
Adversarial review of every plan before it reaches safety. Dissent is surfaced, logged, and must be resolved — not suppressed.
Policy Compliance → Risk Evaluation → Human Approval. In sequence. Every gate produces an auditable decision record. No action bypasses this layer.
Atomic model swap, zero-downtime deployment, tenant routing update, or escalation — depending on the approved plan.
Every approved action is simulated before it reaches production. Forecasts accuracy, cost, risk, and stability. Blocks execution if thresholds are not met.
REST inference with /predict, /predict/batch, /healthz, /readyz and full request tracing.
Directed state graph execution with conditional transitions and immutable state logging.
Every model version, retrain event, and quality gate outcome is logged to MLflow.
Horizontal pod autoscaling, rolling updates, liveness/readiness probes, ConfigMap configuration.
Each tenant routes to its own model version. Canary deployments run at configurable traffic splits with shadow metrics and automatic promotion on quality gate pass.
Every architectural layer has a live instrument panel.
View the Displays →