Operations | Monitoring | ITSM | DevOps | Cloud

Want to start chaos engineering? Don't randomly break stuff and hope for the best. Real chaos engineering starts with defining your system's steady state metrics like latency, throughput, and error rates. Then you form a clear hypothesis about what should happen when failures occur. Next, you inject controlled failures, starting small with single pod kills or network drops, not production meltdowns. Finally, you limit the blast radius by running experiments in safe environments first.

Stop treating system outages like surprises and start preparing for them. While traditional software testing is the bedrock of development, using unit, integration, and regression tests to verify that code meets specific requirements, it only accounts for what we expect to happen. Chaos Engineering takes a different approach by shifting the focus from bug prevention to system resilience. Instead of asking "does this work?", Chaos Engineering asks "how does this survive?" by injecting real-world turbulence like network latency or pod failures directly into production-like environments.

Discover how leading engineering teams proactively build rock-solid applications using Chaos Engineering. Learn why waiting for real outages is risky and how intentionally injecting controlled failures like pod crashes, network latency, and node restarts helps uncover hidden weaknesses before they impact your users. In this short, explore the simple yet powerful practice that turns fragile systems into resilient ones and how Harness makes running chaos experiments effortless and safe with its intuitive Resilience Testing module.

Are myths about chaos engineering preventing your team from building more resilient systems? In this video, Matt Schillerstrom, Director of Product Management at Harness and founding engineer of the chaos engineering program at Target.com, breaks down the three most common misconceptions about chaos engineering. Drawing from his experience building large-scale programs, Matt explains how to move past these myths to build confidence in your infrastructure.

Training is at the heart of every LLM model, but it’s still an application running on an infrastructure, which means it can fail. Our GPU test helps you test your training GPUs so you don’t lose that valuable work. TRANSCRIPT: One of the things we built recently was the GPU Gremlin. So if you are training a bunch of models and you're doing a bunch of GPU testing. You know, we want to give you the tools to be able to go test that, to understand how training the model could fail.

Reliability testing isn’t a one-and-done thing. You need to test on a regular schedule to make sure your system is reliable in the face of changing systems.