Stop Treating Every Red Signal as an Incident

At 18:00, a load-shedding event should not page the on-call engineer simply because a Johannesburg node has disappeared. It should trigger a controlled change in monitoring behaviour: suppress predictable infrastructure alerts, preserve customer-facing SLOs, and confirm that workloads…

The useful role of AI-Augmented Root Cause Analysis Systems

At 02:17, a payment API started returning intermittent 503s. The first alert pointed at Kubernetes CPU. The second blamed Redis latency. A dashboard showed a healthy database, while Loki contained a stream of connection-reset errors from a service…

Why a service list is not a dependency model

The payment API was healthy, the database dashboard was green, and the Kubernetes nodes had plenty of capacity. Yet customers in Gauteng were receiving intermittent checkout failures. The missing clue sat three hops away: a shipping-rate call routed…

Start with the failure boundaries, not the product list

At 02:13 during another round of load-shedding, a Kubernetes cluster in Johannesburg started buffering telemetry. Prometheus kept scraping, but its remote-write queue grew faster than the backup power could stabilise the node pool. By the time the site…

Why advanced correlation matters in a hybrid African estate

At 02:17, a payment API in Johannesburg started returning intermittent 502 responses. The first alert blamed the ingress controller. Five minutes later, a database connection pool alert fired. Then a burst of authentication failures appeared in the logs.…

Start with a failure model, not a dashboard

At 02:17, a payment API looked healthy in Johannesburg. CPU was normal, the Kubernetes pods were ready, and the synthetic check from the local office passed. Customers routed through a European dependency, however, were timing out. The useful…

Why hybrid failures rarely announce themselves

At 02:13, a Cape Town payment service did not fail cleanly. Requests to the on-premises database still succeeded, but the application tier in the cloud began retrying across a congested private link. CPU remained ordinary. Memory looked healthy.…

Stop Treating “Healthy” as a Single Number

At 02:17, a payment platform’s Johannesburg API did not fail cleanly. CPU stayed below 50%, pods remained healthy, and the load balancer returned mostly successful health checks. Yet customers experienced timeouts because a saturated inter-region link was delaying…

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…