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Configure the experiment before you inject any fault: define a measurable steady-state baseline, a falsifiable hypothesis, a narrowly bounded target and duration, complete telemetry, explicit stop conditions, a tested rollback path, named responders, communications, and a run record. Start by rehearsing the plan and its stop behavior in a lower-risk environment. Consider production only after the service demonstrates adequate observability, recovery, and blast-radius control.
The pre-experiment settings that matter most
A chaos experiment is a controlled question about resilience, not an improvised outage. Before starting, write down the answer you want and the limits that must not be crossed. The following settings form a practical control sheet for every run.
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| Setting | What to configure | What to verify before injection |
|---|---|---|
| Objective and scope | Resilience question, service, dependencies, fault, duration, and maximum blast radius | Every affected resource and exclusion is identified |
| Steady state | Customer, business, application, and operational indicators with baseline values | The service is healthy and measurements are available |
| Hypothesis | Expected behavior, recovery mechanism, and disproof signals | Each prediction maps to an observable measurement |
| Fault and target | Action, target identifiers, scope, duration, ramp or backoff, and permissions | The selected resources are exactly the intended resources |
| Observability | Dashboards, logs, metrics, traces, synthetic checks, and onset/recovery alarms | Operators can see both workload and target-component health |
| Guardrails | Customer-impact, workload-health, and component-health thresholds | The system can stop the run and the response has been rehearsed |
| Rollback and recovery | Fault removal, state restoration, automated recovery, and human intervention | Recovery signals and access paths work before the test |
| Readiness and communications | Permissions, ownership, timing, notification list, and escalation channel | Responders know who acts and when the run occurs |
| Schedule and record | Window, timeline, expected observations, run ID, owner, and findings location | The experiment can be audited and reviewed afterward |
1. Define the resilience question and blast radius
Write the question in operational terms
Begin with a question that names a component, a fault, and a time period. A useful form is: “What happens to steady state if this component experiences this fault for this long?” For example, you might ask whether the checkout service continues accepting orders when one availability-zone instance group cannot reach its dependency for five minutes.
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Map the target and everything it can affect
Record the service, environment, account or project, region, cluster, namespace, labels, instance IDs, and dependency paths. Include shared infrastructure: databases, queues, identity systems, network gateways, service meshes, and monitoring components. A fault aimed at one application can consume a shared connection pool or saturate a common node.
Set a maximum blast radius before selecting the fault. Limit the number or percentage of resources, a single zone where appropriate, a short duration, or a small synthetic workload. Define exclusions for unrelated services and a rollback plan if an indirect dependency is affected. The experiment template should make the target unambiguous enough that a second operator can confirm it without guessing.
2. Establish a measurable steady state
Choose indicators that represent service health
Steady state is the normal behavior you intend to preserve, expressed with measurements. Use a mix of indicators:
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- Customer outcomes: successful transactions, completed workflows, availability, and user-facing synthetic checks.
- Business outcomes: orders accepted, messages delivered, or other service-specific completion measures.
- Application signals: throughput, latency percentiles, saturation, queue depth, and error rates.
- Operational signals: resource utilization, replica health, dependency errors, and alert state.
Define the measurement window and percentile. “Latency” is incomplete unless you specify whether it is average, median, 95th percentile, or 99th percentile, and which requests are included. Capture the baseline under representative traffic rather than during a deployment, migration, incident, or unusual promotional peak.
Confirm that the baseline is healthy
Do not start an experiment while an existing incident, degraded dependency, or unresolved alert makes the result ambiguous. Check that dashboards display current data, logs are arriving, traces contain the relevant spans, and synthetic transactions are succeeding. A baseline may include a known, accepted level of faults; zero errors is not a prerequisite if the service’s normal range and customer tolerance are documented.
Attach thresholds to the service, not a generic template
Use historical performance, service-level objectives, and business risk to set limits. A threshold that is safe for an internal batch job may be unacceptable for payments. Write the baseline value, allowed deviation, evaluation window, and whether the condition must persist for one sample or several consecutive samples.
3. Write a falsifiable hypothesis
Describe expected behavior and recovery
A complete hypothesis states the fault, the expected service response, the mechanism that should make it possible, and the measurements that would disprove it. For example: “If two of twelve stateless workers are terminated for three minutes, successful requests will remain within the normal range because the scheduler replaces capacity and the load balancer removes unhealthy workers; the hypothesis fails if transaction success falls below the agreed threshold or recovery does not complete within the stated window.”
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Focus on system outputs rather than an internal promise such as “the retry loop will run.” Retries may run while multiplying load or delaying customers. Measure the result at the user boundary and inspect internal signals to explain it.
Separate prediction from acceptance criteria
Predictions describe what you think will happen; acceptance criteria define what counts as an acceptable result. Include an onset expectation, a maximum tolerated impact, and a recovery deadline. If you cannot identify a measurement that could prove the hypothesis wrong, rewrite it before proceeding.
4. Configure the fault, target, duration, and ramp
Name the exact action
Specify whether the run will terminate processes, add latency, drop packets, restrict bandwidth, exhaust a resource, alter dependency responses, or apply another fault. Record the tool action and its parameters, including units. “Inject latency” should become “add 200 milliseconds to requests from service A to dependency B.”
Constrain target selection
Use immutable identifiers or narrowly scoped labels, and include the environment and region in the selector. Preview the matched resources immediately before execution. Have a second person confirm the target when the action can affect production or shared infrastructure. Ensure the experiment identity has only the permissions it needs; broad administrative access increases the chance of an accidental target.
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Define start and end times, a maximum duration, and what happens if the orchestrator loses contact. If the tool supports a gradual ramp, start with a small percentage or one resource, observe, then increase only if guardrails remain clear. Specify retry and backoff behavior so a failed control call does not repeatedly reapply the fault. A timeout must leave the system in a known state rather than waiting indefinitely.
Start in a lower-risk environment
For an application’s first experiment, use a close pre-production environment to validate targeting, telemetry, permissions, stop behavior, and recovery. The environment should resemble production enough to make the signals meaningful. Differences in traffic, data volume, topology, and dependency limits mean a successful rehearsal does not prove production safety; it proves that the procedure and controls work under the rehearsed conditions.
5. Make observability part of the experiment
Watch both the workload and the faulted component
Put service-level indicators and component-level telemetry on the same operational view. Track customer success and latency alongside CPU, memory, network, pod or instance health, queue depth, dependency responses, and scheduler activity. Logs should identify the experiment and affected resources; traces should show where latency or errors enter the request path.
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Add an independent user-facing check
A synthetic monitor that performs a representative workflow can reveal a failure that aggregate infrastructure metrics hide. Choose a check that is safe to run at the planned frequency and that distinguishes a real customer failure from a test-account or third-party problem. Record its normal result before injection.
Alarm on onset and recovery
Configure alarms for both deterioration and return to normal. A run is not complete merely because the fault has been removed: the service may have stale connections, a growing queue, or a damaged cache that fails later. Define how long indicators must remain within bounds before declaring recovery, and ensure the monitoring system itself is not the component being disrupted without an alternative view.
6. Set guardrails and automatic stop conditions
Use three classes of limits
- Customer-impact limits: transaction success, availability, user-facing latency, or synthetic workflow failure.
- Workload-health limits: throughput collapse, queue growth, error-rate increase, saturation, or loss of healthy capacity.
- Target-component limits: CPU or memory pressure, replica loss, dependency failures, network errors, or state corruption indicators.
For each limit, state the threshold, evaluation period, owner, and action. “Stop if things look bad” is not executable. “Stop when checkout success is below 99.5% for two consecutive one-minute evaluations” gives an operator or automation a clear rule, provided that value reflects the service’s own baseline and risk tolerance.
Decide who or what stops the run
Prefer an automated stop tied to monitored alarms where the experiment system supports it, with a human abort available. The stop action should halt further fault injection and invoke the rollback path. Test the alarm-to-stop chain in a low-risk environment: create a harmless condition, confirm the alarm changes state, verify the experiment stops, and check that the fault is removed.
AWS Fault Injection Service (AWS FIS) uses CloudWatch alarms for stop conditions and provides safety controls to halt running experiments and prevent new experiments in a Region. These controls still require correct alarm definitions, permissions, and operator ownership; enabling a feature does not substitute for testing it.
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Document the fastest way to cancel the experiment if telemetry is delayed or the orchestration system behaves unexpectedly. Include the console path or command used by authorized responders, the person who can execute it, and the expected confirmation. Store this procedure where it remains accessible during an incident.
7. Prepare rollback and recovery
Define how the fault is removed
Some actions have a direct inverse, such as deleting a network rule or restoring normal latency. Others require replacement, failover, cache warming, queue draining, or state repair. Write the removal operation and its timeout into the run plan. If the fault tool is interrupted, know whether it cleans up automatically or leaves a persistent condition.
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Identify the known-good state
Describe what “recovered” means for resources and data: all replicas healthy, connections re-established, queues below a limit, error rates normal, and customer workflows succeeding. Include any eventual-consistency delay. Do not close the run when the injector reports success; close it when the service and dependencies have returned to the defined state.
Assign human intervention
Name the on-call engineer, incident commander, and subject-matter owner who can intervene if automation fails. Verify they have credentials, network access, and a tested path to the affected system. AWS Well-Architected guidance recommends experiment tooling that tracks state, emits logs, and provides rollback mechanisms; a custom script should meet the same practical requirements before it is trusted.
8. Confirm readiness, ownership, and communications
Check operational prerequisites
- Required experiment, cloud, cluster, and observability permissions are active.
- Dashboards, alarms, logs, and traces open for every operator who needs them.
- Time synchronization and run identifiers allow events to be correlated.
- Support, operations, reliability, and dependency owners know the planned window.
- An escalation channel and incident process are available throughout the run.
Choose a responsible window
Avoid deployments, migrations, peak commercial events, major launches, holidays, and periods when the response team is unavailable. The safest window is not necessarily the quietest: extremely low traffic can hide capacity and failover behavior. Choose a period that provides representative load while preserving enough staffing and business margin to respond.
Communicate expected signals and stop authority
Tell participants what fault will occur, which dashboards to watch, what transient symptoms are expected, and which thresholds require an immediate stop. State who has final authority to abort; any responder who sees unacceptable customer impact should be able to trigger the emergency control without waiting for consensus.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Schedule and document the run
Put the complete plan in one record
Include the objective, steady-state values, hypothesis, target selectors, fault parameters, ramp, timeline, expected observations, stop conditions, rollback procedure, owners, communication channel, and links to dashboards. Record the planned start and end in a shared calendar or change system so a responder can find the context during an alert.
Capture evidence while the experiment runs
Preserve the experiment run ID, configuration version, alarm transitions, logs, graphs, trace examples, operator actions, and timestamps. Note deviations from the plan rather than silently adjusting them. A result without its parameters cannot be reproduced or safely compared with a later run.
Review outcomes against the hypothesis
Classify the result as supported, disproved, inconclusive because telemetry was insufficient, or invalid because the baseline was not healthy. Record customer impact, time to detection, time to stop, time to recover, unexpected dependencies, and follow-up engineering work. Do not broaden the fault or increase duration in the next run until the previous result is understood.
Production or pre-production: a staged decision
Guidance differs in emphasis, but it is not contradictory. The Principles of Chaos Engineering favors production experiments because real traffic and topology expose behavior that test environments miss, while requiring blast-radius minimization. AWS Prescriptive Guidance advises against running an application’s initial chaos experiment in production; it recommends learning in a lower-level environment and progressing after verifying that the system can absorb, adapt to, and recover from the fault. AWS Well-Architected guidance likewise recommends checking scope and stop-condition behavior outside production first, while allowing carefully controlled production experiments, including canary or synthetic-traffic approaches where feasible.
A practical progression is:
- Validate the experiment definition, target selector, telemetry, alarm, stop action, and cleanup in a close pre-production environment.
- Repeat with a small scope, short duration, and representative synthetic or canary traffic if production behavior is materially different.
- Review evidence that responders can detect, stop, and recover from the fault within the business risk limit.
- Run a tightly bounded production experiment only with explicit ownership, communication, rollback, and an acceptable customer-impact budget.
- Increase one variable at a time—scope, duration, fault intensity, or traffic—while preserving the same guardrails.
Production is therefore neither mandatory for every first test nor categorically forbidden. It is a later stage justified by evidence and risk tolerance.
Illustrative threshold values: use them only as examples
An AWS planning document for a pet-adoption application proposes an average Largest Contentful Paint of 2.5 seconds or less, 99th-percentile latency of 4.0 seconds or less, and a baseline of 5,000 concurrent users. Its example hypothesis asks what would happen if application pods experienced or caused more than 60% CPU utilization for 10 minutes under normal production-level traffic. AWS presents these as values for that example workload; they are not universal safe settings.
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| Illustrative value | Context and limitation |
|---|---|
| Average LCP ≤ 2.5 seconds | Example customer-performance criterion in the AWS pet-adoption planning document; publication year is not stated on the cited sample page. |
| 99th-percentile latency ≤ 4.0 seconds | Example application latency criterion for that workload, not a cross-industry target. |
| 5,000 concurrent users | Example baseline load used by the same planning document. |
| More than 60% CPU for 10 minutes | Example fault condition and duration applied to application pods under normal production-level traffic. |
Translate any example into your own service’s measurements before using it. Confirm which requests, users, regions, and time windows are included, and obtain business approval for the resulting impact budget.
How to evaluate an experiment tool or script
Choose tooling by control capability rather than by the number of fault types in its catalog. Check whether it can:
- Narrowly target resources by stable identifiers, labels, environment, and region.
- Set a precise fault scope, duration, ramp, timeout, and cleanup behavior.
- Associate the run with measurable steady-state indicators and a hypothesis.
- Stop automatically when monitored thresholds are breached, while exposing a human abort.
- Roll back or recover, track experiment state, emit useful logs, and preserve a run ID.
- Support safe rehearsal outside production and provide clear success or failure status.
- Fit the team’s cloud, container, network, and service architecture without bypassing normal access controls.
AWS identifies AWS FIS as an option for AWS workloads. Custom scripts can be appropriate, but they need workload-state awareness, structured logging, rollback, and tested stop conditions. A script that merely issues a fault command and exits leaves the most important safety work to chance.
A final pre-flight checklist
- The resilience question names the component, fault, duration, and intended outcome.
- Targets, dependencies, exclusions, permissions, and maximum blast radius are confirmed.
- Steady-state indicators have current baseline values and acceptable ranges.
- The hypothesis can be disproved by measurements visible to the operators.
- Dashboards, logs, traces, synthetic checks, and onset/recovery alarms are working.
- Customer, workload, and component guardrails have explicit thresholds and evaluation windows.
- Automatic stop behavior and an independent emergency abort have been tested.
- Fault removal, state restoration, recovery criteria, and human escalation are documented.
- Operations, support, reliability, and dependency owners know the schedule and signal expectations.
- The run record includes configuration, timeline, owner, run ID, evidence location, and review plan.
Chaos Engineering is “the discipline of experimenting on a system in order to build confidence in the system’s capability to withstand turbulent conditions in production,” according to the Principles of Chaos Engineering. That confidence comes from controlled measurement and recovery—not from injecting a fault by itself. AWS Well-Architected states: “You must establish and monitor guardrails to ensure the experiment does not impact production traffic or other systems beyond acceptable limits.” Configure those guardrails, prove that they stop and clean up the run, and only then decide whether the next experiment belongs in production.
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