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From Chaos Engineering to Resilience Testing: Why We're Expanding How Teams Validate Reliability | Harness Blog

At Harness, we’re committed to helping teams build and deliver software that doesn’t just work – it thrives under pressure, scales reliably, and recovers swiftly from the unexpected. Today, we’re taking the next step in that mission by evolving our Chaos Engineering module into Resilience Testing. This evolution reflects how reliability is tested in practice today.

Disaster Recovery Testing by Gremlin

Do you know how your system will respond when major outages strike? Disaster Recovery Testing safely simulates real catastrophic failures across your entire system. You can centrally and easily run zone, region, and datacenter-scale reliability tests across your entire organization simultaneously for disaster recovery, business continuity, compliance verification, and more. With Disaster Recovery Testing, tests that used to take engineering-months and dozens of experts can be done safely and securely in hours by a single person.

Reliability Resolutions: How to build effective reliability programs that won't fade away

Did you know the third week of January is the most common time for people to fail New Year’s Resolutions? It doesn’t matter whether it’s exercising more, learning a new language, or just trying to drink less coffee, that initial surge of fresh New Year’s energy is fading, and if you want to make a resolution stick, this is the key time to make a lasting change. The same is true with any reliability resolutions you might have made.

Recommended Experiments for Production Resilience in Harness Chaos Engineering | Harness Blog

This guide covers battle-tested chaos experiments for Kubernetes, AWS, Azure, and GCP to help you validate production resilience before real failures happen. Start with low blast radius experiments (pod-level) and gradually progress to higher impact scenarios (node/zone failures), always defining clear hypotheses and using probes to measure results. Building reliable distributed systems isn't just about writing good code. It's about understanding how your systems behave when things go wrong.