chaos engineering.

Where chaos engineering fits on a long engagement.

The point is not breaking things; it is checking a specific belief. A useful experiment states a hypothesis — "if this instance is terminated, requests continue with no user-visible error" — and then terminates it. Either the belief holds, or an assumption that was going to fail eventually fails now, on a Tuesday, with everyone watching.

Readiness matters more than tooling. A system without reliable monitoring cannot run these experiments, because nobody will be able to tell what the injected failure caused. Chaos engineering is something a mature system does, not a route to becoming one.

What an assigned team does with chaos engineering.

Starting in production is the common mistake. Staging experiments build the practice and catch the obvious gaps; production experiments with a small blast radius and a tested abort come later, once the team trusts both the process and its monitoring.

Sequencing that properly is part of how reliability work is planned, as described in how an assignment runs.

What we use chaos engineering for.

• Verifying failover actually fails over Testing the mechanism deliberately rather than discovering it during an outage.
• Dependency failure handled gracefully Third-party latency and errors injected, so timeouts and fallbacks are proven.
• Monitoring proven adequate Checking that an injected failure is actually visible, which is often the first real finding.

How chaos engineering capacity is assigned.

Resilience testing is assigned under devops as a service, sequenced after monitoring is trustworthy rather than before.