agent-health-monitoring

Detect silent failures by monitoring agent vital signs like latency, error rate, step growth, and token context pressure. This Skill applies liveness probing and statistical anomaly detection to identify unresponsive or degraded agents.

AI agent monitoring uses metrics like P50/P95/P99 latency, error rate, tool success rate, step count, token consumption, and context pressure. These signals are evaluated against statistical thresholds to detect anomalous behavior.

Severity-based alerting routes incidents by P0 through P3 severity to appropriate channels. You define alert rules using statistical thresholds for metrics like error spikes and latency degradation to trigger incident response workflows.

Generate operational dashboards and incident response runbooks by analyzing agent health metrics and detected anomalies. This produces the panel set and playbooks needed to investigate and recover from unresponsive agents or token budget anomalies.

Liveness checks verify an agent is alive, while readiness checks confirm it can accept work. Deep checks validate the agent is capable of producing valid outputs by running diagnostic test tasks.

Yes, telemetry instrumentation is required to monitor agent health. It provides the operational health signals like latency metrics and token consumption needed to detect anomalies and trigger severity-based incident response workflows.