This workshop focuses on using n8n as a reaction and decision engine for real DevOps environments.The emphasis is not on how to deploy n8n, but on how to use it correctly in production to respond to Kubernetes events, Prometheus and Grafana alerts, and operational incidents.Participants will design workflows that:
React to real infrastructure signals
Enrich alerts with live cluster data
Make safe, conditional decisions
Perform controlled remediation actions
Who Should Attend
Experienced DevOps Engineers and Developers with k8s , docker , prometheus and grafana experience
Prerequisites
Great Kubernetes knowledge
Familiarity with monitoring concepts (metrics, alerts)
Some experience with n8n required
Course Contents
vent-Driven DevOps Automation
The workshop begins by reframing how automation is used in production systems.Participants explore the difference between:
CI/CD pipelines
Controllers and operators
Event-driven reaction workflows
The session explains where n8n fits in a modern DevOps stack and why it should be used as a decision layer
Reacting to Kubernetes Events
This session focuses on reacting to Kubernetes failures without polling or cron jobs.Participants work with real Kubernetes failure scenarios such as pods entering CrashLoopBackOff or deployments failing to stabilize. Instead of trusting incoming signals blindly, workflows are designed to validate the current cluster state using the Kubernetes API before acting.
Prometheus Alerts to Action
Prometheus alerts are often noisy and poor in explaining the cause. This session teaches how to treat alerts as signals, not commands.Participants receive alerts from Prometheus, then enrich them using live PromQL queries. Workflows distinguish between short-lived spikes and sustained issues before deciding on remediation actions such as restarts or scaling.
Grafana Alerts as Incident Triggers
This session focuses on Grafana as an alert source rather than a visualization tool.Participants build workflows that receive alerts from Grafana, extract structured information, and correlate it with Kubernetes state and recent events.This allows n8n to act as an incident triage layer rather than a simple alert forwarder as in many problematic setups is being built
Stateful Incident Handling
Many production incidents are repetitive or flapping in nature. This session introduces the concept of stateful workflows using n8n execution data.This prevents automation loops and reduces alert fatigue.
Controlled Auto-Remediation Patterns
A common scenario explored is a failed deployment where n8n verifies rollout status, requests approval, and only then performs a rollback.The emphasis is on automation with accountability.
Multi-Signal Correlation
Production incidents rarely have a single cause. This advanced session teaches how to combine multiple signals before taking action.For example, a node pressure alert is evaluated alongside pod density, eviction events, and node age before deciding whether to drain the node or escalate.This session reinforces the idea that n8n workflows should think like an experienced SRE.
