chaos-engineering

Chaos engineering is the discipline of validating system resilience by deliberately injecting faults into production-like environments to confirm systems respond correctly under failure conditions. It identifies weaknesses in microservices and critical dependencies before real outages occur.

You can run automated resilience experiments by using the chaos_schedule.py script to generate a quarterly chaos engineering plan. This automates game days and staging runs to validate circuit breakers, bulkheads, retries, and fallback paths across your services.

Yes, the approach supports fault injection using tools like Istio fault injections and Toxiproxy. It applies to staging experiments and quarterly resilience checks across microservices, simulating dependency outages, latency, pod failures, and network partitions.

You define steady-state measurements and abort conditions by establishing observable metrics before fault injection. The Skill enforces defined steady-state measurements, abort criteria, an experiment catalogue, and observability scaffolding to ensure reliable postmortems.

The best way to simulate dependency outages and network partitions is using a standard experiment catalogue with fault injection tooling. It enforces observable steady-state metrics and abort criteria to safely test dependency outages, latency, and pod failures in staging environments.

You should not use chaos engineering without defined steady-state measurements and abort conditions. It requires production-like environments to safely inject faults; lacking proper observability scaffolding or runbooks for postmortems makes resilience experiments risky and inconclusive.