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…

Unified Monitoring for Multi-Cloud Ecosystems starts with service context

At 02:17, a payment API in Johannesburg began returning intermittent 504s. The application looked healthy in AWS, the Kubernetes dashboard in Azure showed no obvious failures, and the on-premise firewall reported normal traffic. The useful clue was not…

Predictive Failure Detection for Hybrid Clouds starts with failure paths

At 02:17, the cloud-facing checkout service was still returning 200s. The useful warning was elsewhere: packet loss on the Johannesburg-to-Dublin link had climbed slowly, one on-premise queue was growing, and the UPS runtime had fallen below the time…