Core Definition

Chaos Engineering is the discipline of experimenting on a software system in production in order to build confidence in the system's capability to withstand turbulent and unexpected conditions. Rather than waiting for failures to occur naturally—often at the worst possible times—Chaos Engineering proactively injects controlled faults in a managed way to uncover hidden weaknesses before they become critical outages.

The Foundation: Steady State

Before any experiment, teams must establish a clear understanding of what "normal" looks like—the steady state. This involves defining measurable metrics like request success rates, response latencies (average, P90, P95, P99), resource utilization, queue lengths, and error counts. Your hypothesis will state that the system maintains this steady state even when subjected to specific types of failure.

Key Goals

• Identify Weaknesses: Uncover hidden bugs, bottlenecks, and cascading failure points that traditional testing might miss.
• Improve Resilience: By understanding how systems fail, engineers can build more robust and fault-tolerant architectures.
• Verify Assumptions: Confirm that monitoring, alerting, and auto-remediation systems work as expected during actual failure scenarios.
• Increase Confidence: Build confidence in the system's ability to handle unexpected events, leading to more reliable services and better user experiences.
• Reduce MTTR: Practicing incident response through chaos experiments improves team preparedness and speeds up recovery during real incidents.

Why It Matters

In distributed systems, failures are inevitable. The question is not whether your system will fail, but when—and whether your team is prepared to respond. Chaos Engineering answers that preparation question with evidence.