Reliability starts with the dependency graph, not the dashboard

The payment API looked healthy until customers in Johannesburg began receiving timeouts. CPU was below 40 percent, the Kubernetes nodes were ready, and the Grafana dashboard was mostly green. The failure was further downstream: a service in eu-west…

Start with the questions the platform must answer

At 18:03 on a weekday evening, a payment service started timing out in Johannesburg. The first alert blamed API latency. The second blamed a Prometheus instance. Neither was the real problem: load-shedding had moved part of the workload…

Correlation starts with evidence, not an LLM

At 02:17, a payment API started timing out in Johannesburg. Prometheus reported rising latency, Loki showed a burst of connection errors, and Tempo traced the slow requests to a service calling a database in Cape Town. Five minutes…

Why a dashboard did not explain the outage

At 18:07 on a Tuesday, a payment service started timing out in Johannesburg. The first alert blamed CPU. The second blamed Redis. Neither was useful: the real problem was elevated latency on the route to an upstream service…

Start with the decision, not the dashboard

At 17:42, a load-shedding event changed the shape of a production incident. The primary application remained healthy, but a power transition restarted several edge services, increased retry traffic and pushed latency towards eu-west above the alert threshold. The…

Self-Healing Infrastructure Monitoring Models Start with Symptoms

At 18:00, a Kubernetes node in Johannesburg stopped reporting metrics. The first alert looked like a hardware failure. The second looked like a Prometheus outage. The real problem was load-shedding: the node had rebooted, the local collector was…

Start with a telemetry budget, not a surprise invoice

The bill arrived after a harmless-looking configuration change: a Kubernetes team reduced a Prometheus scrape interval, added pod and request labels, and enabled debug logging for a release. Nothing failed. Dashboards remained green. Yet the month-end invoice was…

Start with an SLO, not a dashboard

At 18:00, the API error rate stayed below its alert threshold while customers in Gauteng reported failed payments. The real problem was not the application: a brief power interruption had caused an edge router to flap, traffic was…

AI-Augmented Root Cause Analysis Systems Need Evidence, Not Just Answers

At 02:17, a payment API began timing out for customers in Johannesburg. Prometheus showed elevated latency, Loki contained a growing number of connection errors, and Tempo displayed broken traces between the checkout service and a third-party fraud provider.…

Why a service map is not yet dependency visibility

The payment API looked healthy: CPU was normal, Prometheus showed no obvious saturation, and the checkout dashboard stayed mostly green. Customers in Johannesburg still experienced failed transactions because the API was waiting on a database connection in Cape…

Start with the shape of the metrics problem

At 02:17, a load-shedding event switched a Johannesburg data centre to generators. The applications stayed up, but the monitoring stack did not: Prometheus queues filled, remote-write retries multiplied, and the central metrics cluster received a burst of delayed…

The incident is a graph, not a pile of log lines

At 02:13, a payment service started returning intermittent 502 responses. The first dashboard showed a modest rise in API latency. The second showed nothing unusual because the affected requests were spread across three Kubernetes nodes, an on-premise database…