How do you run your first production chaos experiment?

WHAT THIS TESTS Whether you can combine experimental rigor with organizational responsibility, since a first production chaos run is as much about trust and process as about the fault itself.

A GOOD ANSWER COVERS Planning: pick a small, well-understood scenario, ideally a suspected weakness you can reason about, rather than the scariest failure. Confirm prerequisites first, that observability is in place to see impact and that a tested rollback or kill switch exists. Define the steady-state hypothesis in measurable terms and write down the abort criteria up front. Communication: brief stakeholders, product owners, and especially the on-call, sharing what you will inject, when, the expected impact, the blast radius, and how to stop it; pick a low-traffic window for the first run and have the right people watching. Execution: start with the smallest blast radius, a tiny traffic slice or single instance, inject the fault, and watch guardrail metrics in real time with the automated abort armed. If a guardrail breaches, abort and revert immediately. Analysis: compare outcomes to the hypothesis, whether it held or was disproven, capture findings, file action items for any gaps, and share a short writeup to build organizational confidence to do more.

COMMON WRONG ANSWERS Starting with a dramatic, high-impact fault to prove value. Skipping stakeholder and on-call communication. Running without observability so you cannot see impact. No rollback or abort criteria defined beforehand. Treating a disproven hypothesis as a failure rather than a valuable finding.

LIKELY FOLLOW-UPS How do you choose the first scenario. What goes in the abort criteria. How do you get buy-in from a nervous team. How do you decide to widen the radius next time.

ONE CONCRETE EXAMPLE For a first run you hypothesize that adding 100ms latency to the recommendations call keeps checkout completion within steady state. You confirm dashboards and a mesh-based kill switch, brief the on-call and product owner, and schedule a low-traffic window. You inject latency on 1 percent of sessions, watch completion rate live, and the hypothesis holds. You publish a short writeup, and the visible safety and learning earn approval to expand scope next quarter.