The Tool Desk
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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 fault, complete telemetry, explicit stop thresholds, a tested rollback path, named responders, and a documented schedule. These settings turn an interruption into a controlled learning exercise rather than an uncontrolled outage.
The exact numbers must come from the service’s normal performance and risk tolerance. A latency or CPU threshold copied from another system is not a safety control. Start by rehearsing the plan and stop behavior in a lower-risk environment; move to production only when the team can observe, halt, and recover the experiment with an acceptable blast radius.
The pre-run settings checklist
Every experiment plan should answer the same operational questions before execution. The table below is a compact specification you can use in an experiment template.
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| Setting | What to configure | Why it matters |
|---|---|---|
| Objective and scope | Resilience question, service, dependencies, fault, duration, maximum blast radius, exclusions | Prevents an experiment from expanding beyond the question it is intended to answer |
| Steady state | Customer, business, application, and operational indicators with a healthy starting window | Provides the comparison point for deciding whether the system changed materially |
| Hypothesis | Expected behavior under the named fault and measurements that would disprove it | Makes the run falsifiable instead of turning it into unstructured monitoring |
| Fault and target controls | Action, target identifiers, scope, duration, ramp-up or backoff, and a target-selection check | Limits what can be affected and makes the injection repeatable |
| Observability | Dashboards, metrics, logs, traces, synthetics, and onset/recovery alarms | Lets operators see both customer impact and component behavior |
| Guardrails and stop conditions | Thresholds, stop authority, automation, and a tested halt path | Stops unacceptable impact before it becomes a prolonged incident |
| Rollback and recovery | Removal method, known-good state, manual intervention, and recovery signals | Defines how the system and the team return to normal if automation fails |
| Readiness and communications | Access, permissions, on-call ownership, notification list, and escalation channel | Ensures someone can act when an alarm fires |
| Schedule and record | Window, run ID, expected observations, result owner, and post-run notes | Creates an auditable record and prevents high-risk timing surprises |
1. Define the resilience question and the experiment’s boundary
Write the question in a form that can be answered by one run. A useful pattern is: “What happens to steady state if this component experiences this fault for this period under this workload?” The question should identify an outcome that matters to users or the business, not merely whether an internal process stayed alive.
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Record the objective
State the service behavior you want to understand: for example, whether checkout remains usable when one application instance is unavailable, or whether a queue consumer recovers after a dependency timeout. Include the reason the question matters now, such as a recent architecture change or an unverified failure mode. Keep the objective narrow enough that a pass or fail decision is possible.
Map the scope and dependencies
Name the service, account or cluster, region, environment, and component identifiers that may receive the fault. Map shared databases, queues, network paths, identity systems, and observability components. A target that looks local may rely on shared infrastructure; that dependency belongs in the impact assessment.
Set a maximum blast radius in advance. Specify the number or percentage of instances, zones, tenants, requests, or synthetic users that may be affected. List exclusions for unrelated services and define how the run will be stopped if the fault begins propagating outside the intended boundary.
Specify duration and timing
Set a start condition, a maximum duration, and any ramp-up or backoff. A fixed duration makes results comparable, while a maximum duration limits exposure if the experiment controller loses contact. Choose a window that avoids releases, migrations, peak business events, and other periods when a normal incident would already be difficult to handle.
2. Establish a measurable steady state before the fault
Steady state is the normal behavior against which the experiment is judged. It is not necessarily zero errors or zero latency variation. A healthy service can have a known, tolerated level of faults; the baseline must describe that reality rather than an impossible ideal.
Choose indicators that represent the service
Use a small set of indicators spanning the customer and the system:
- Customer outcomes: successful transactions, completed journeys, availability, or a user-facing synthetic check.
- Business outcomes: order completion, payment authorization, message delivery, or another outcome whose loss has direct business meaning.
- Application outcomes: request throughput, latency percentiles, timeout rate, and error classes.
- Operational outcomes: CPU, memory, queue depth, saturation, replica health, and dependency error rates.
Record the measurement window, workload level, aggregation, and accepted range for each indicator. Percentiles are generally more informative for tail latency than averages, but use the statistic that reflects the service’s established service-level objective.
Verify health before starting
Check that the service is within its normal range immediately before injection. Do not begin while an unresolved incident, deployment, scaling event, backlog, or dependency degradation is already changing the indicators. Capture a baseline snapshot so that the run can distinguish fault effects from pre-existing drift.
Define what “changed” means
For every indicator, state the comparison rule: a sustained breach, a percentage change from baseline, a failed transaction count, or a recovery-time limit. A single noisy data point should not automatically fail an experiment unless the risk model requires immediate action. Conversely, an average can hide a small group of users experiencing severe tail latency, so include a customer-facing measure where appropriate.
3. Write a falsifiable hypothesis
The hypothesis connects the fault to an expected system response. It should describe outputs, not implementation wishes. Include why the architecture is expected to preserve or recover the service and which observation would disprove that expectation.
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A practical hypothesis format
Use a sentence such as: “If fault is applied to target for duration while workload is present, then customer outcome will remain within defined range because recovery or isolation mechanism; the hypothesis is disproved if measurement exceeds threshold for window.”
This format forces the team to identify the observation that would count as failure. “The pods should be resilient” is not testable; “successful checkout remains at least within the established baseline range while one instance is unavailable” is.
Separate expectation from implementation
A system may satisfy the hypothesis through a mechanism different from the one designers anticipated. Judge the externally visible result first, then use traces and component metrics to explain it. Do not declare success merely because a replacement instance started or an autoscaler reacted if users still experienced failed transactions.
4. Configure the fault and target controls
The injection definition is a safety boundary, not just a technical command. It must be possible for another operator to read the plan and know exactly what will be changed.
Name the fault precisely
Record the action, parameters, duration, and any ordering. Examples include terminating a selected instance, adding latency to a dependency path, exhausting a controlled resource, or denying a narrowly scoped network route. State whether the action is one-time, repeated, or continuously enforced.
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Prefer stable identifiers, labels, tags, or an explicit allow-list over a broad resource query. Confirm the resolved target set immediately before execution and record it with the run ID. If the target count is zero, unexpectedly large, or includes an excluded environment, stop before injection.
Start with the smallest useful blast radius
For an application’s first experiment, begin with one instance, one zone, a small tenant subset, or synthetic traffic where the question permits it. Increase scope only after the first run demonstrates that telemetry, alarms, stop behavior, and recovery work as intended. A smaller target is not automatically safe if it sits on shared infrastructure, so include dependency analysis in the approval.
Make timing behavior explicit
Document warm-up, ramp-up, dwell, retry, and backoff behavior. A tool that retries a failed action can create a larger fault than the operator expects. Set a hard maximum duration and ensure the experiment controller can report whether the action is pending, active, completed, or failed.
5. Prepare observability for both impact and recovery
Observability must be available to the people running the experiment before the fault begins. If the team cannot see steady state, target behavior, and recovery, it cannot evaluate the hypothesis or prove that the run was controlled.
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Place the key customer, application, dependency, and infrastructure indicators on a dashboard with a common time range. Mark the planned injection and stop events so that a later review can align metric changes with actions. Include target-level views for the resources receiving the fault and a service-level view for effects outside the target.
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Instrument the full signal set
- Metrics: throughput, latency percentiles, errors, saturation, queue depth, and health counts.
- Logs: fault application, retries, failover, rejected requests, and recovery events, with the experiment run ID where possible.
- Traces: dependency timing and error propagation across the request path.
- Synthetic monitoring: a user-facing transaction when real traffic alone cannot reveal a failure quickly or safely.
- Alarms: separate signals for failure onset, unacceptable customer impact, and recovery completion.
Check that clocks, labels, and sampling are consistent enough to correlate the signals. A dashboard that updates after the maximum exposure period is not an effective guardrail.
Verify recovery visibility
Define the evidence that the service has returned to a known state: error rates back within baseline, queues drained, replicas healthy, and synthetic transactions succeeding. Recovery should be observable independently of the component that was faulted; otherwise a damaged monitoring path can falsely report success.
6. Set guardrails and automatic stop conditions
Guardrails translate risk tolerance into an action. Set thresholds for customer impact, workload health, and target-component health, then name who or what stops the experiment when a threshold is breached.
Choose thresholds from the baseline
Use the established steady-state range, service-level objectives, contractual limits, and business tolerance. Define both a trigger and a persistence rule where appropriate, such as a breach for a specified number of evaluation periods. For catastrophic signals, use an immediate stop even if the breach lasts only one observation.
Assign stop authority
Specify the automated controller, the primary operator, and the backup operator. The operator must know which control halts the active fault and which control prevents new experiments from starting. Do not rely on an informal agreement that someone will notice a dashboard.
Test the stop path before increasing risk
In a lower-risk environment, deliberately trip a test threshold and verify that the experiment stops, the fault is removed or contained, an alert reaches the responsible channel, and the operator can identify the final state. AWS Fault Injection Service (FIS) uses CloudWatch alarms for stop conditions and provides safety controls to halt running experiments and prevent new experiments in a Region. Configure and test those controls rather than treating them as a substitute for a runbook.
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.” The sentence describes an operating obligation: alarms are useful only when they are connected to a tested action and an accountable responder.
7. Define rollback and recovery before injection
Rollback is the path out of the experiment; recovery is the return to a known service state. Write both paths even when the fault is expected to self-heal.
Describe fault removal
State the exact reversal for each action: restore a setting, remove a network rule, terminate the experiment, return capacity, or allow a controlled timeout to expire. Identify what happens if the controller is unavailable and who has the permissions to perform the manual reversal.
Define the known-good state
List the conditions that must be true before declaring recovery: customer indicators within baseline, healthy target and dependency counts, no growing backlog, and completed failover or reconciliation. Include any data-integrity checks required after the fault.
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Plan for partial recovery
Some systems recover service availability while leaving retries, queues, or replicas unhealthy. Add a decision branch for that case: extend observation, reduce load, restore capacity, or invoke incident response. The experiment ends only when the service and its supporting components meet the recovery criteria, not merely when the injection command returns.
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8. Confirm operational readiness and communications
A technically correct experiment can still cause an incident if the people with authority are unavailable. Confirm access to the experiment system, target environment, dashboards, logs, traces, alarms, and rollback controls. Verify that permissions are valid for the selected region and account and that emergency credentials or break-glass procedures are understood.
Name the response roles
- Experiment owner: accountable for the question, scope, and result.
- Operator: starts, observes, and stops the run.
- Service owner: confirms customer and dependency impact.
- Incident authority: can suspend the experiment and declare an incident.
- Recorder: preserves timestamps, run ID, observations, and decisions.
One person may hold several roles in a small team, but the responsibilities must still be explicit.
Notify affected groups
Tell operations, reliability, support, service owners, and other responsible parties when the run will occur, what signals they may see, and how to report adverse effects. Include the escalation channel and the planned stop time. Avoid sensitive periods such as major launches, migrations, or known capacity constraints.
9. Schedule and document the run
The experiment record should be complete before approval. Include the objective, steady state, hypothesis, fault, target set, duration, workload, expected observations, guardrails, stop authority, rollback, communications, and result owner.
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Preserve execution evidence
Capture the experiment identifier, resolved targets, configuration version, start and stop timestamps, alarm events, operator actions, and final state. Keep the baseline and post-run graphs with the record. This evidence lets the team distinguish a genuine resilience result from a monitoring or execution error.
Define the review questions
Before the run, decide how findings will be classified: hypothesis supported, disproved, inconclusive because telemetry was insufficient, or invalid because the starting state was unhealthy. Assign an owner and a review date for corrective work. Do not silently rerun an inconclusive experiment with a larger blast radius.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Production or pre-production: choose a staged path
There is no universal rule that chaos experiments must always run in production or must never run there. Production traffic and dependencies differ from test environments, so the Principles of Chaos Engineering favors learning from production conditions 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 first and progressing after verifying that the system can absorb, adapt to, and recover from the fault. AWS Well-Architected likewise recommends checking scope and stop-condition behavior outside production before a controlled production run.
Stage 1: rehearse in a close lower-risk environment
Use an environment with representative architecture, realistic telemetry, and a workload that exercises the failure path. Verify target selection, dashboards, alarm evaluation, stop behavior, rollback, permissions, and communications. A toy environment that omits the dependency you are testing cannot validate the plan.
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Move to production only when the service owner accepts the residual risk and the run has a narrow target, tested stop controls, complete observability, and an available response team. Canary or synthetic-traffic approaches can reduce exposure when they still exercise the relevant path. Keep the first production run smaller and shorter than the maximum the architecture might tolerate.
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Stage 3: expand only on evidence
Increase duration, target count, or fault intensity one dimension at a time. Recheck the baseline and business calendar before each run. A successful lower-level rehearsal proves the controls worked in that environment; it does not prove that production has the same capacity or dependency behavior.
AWS sample thresholds: useful illustration, not a universal standard
An AWS planning example for a pet-adoption application uses these illustrative values:
| Example value | How it is used in the example | Qualification |
|---|---|---|
| 2.5 seconds average Largest Contentful Paint | Customer-facing performance criterion | Illustrative value for that application; not a general web target |
| 4.0 seconds 99th-percentile latency | Tail-latency criterion | Illustrative value tied to the sample workload |
| 5,000 concurrent users | Baseline workload | Example traffic level, not a default load for another service |
| More than 60% CPU utilization for 10 minutes | Hypothesized condition affecting application pods under normal production-level traffic | Example fault condition; it must be recalculated from the target service’s own capacity and risk tolerance |
The same sample discusses request-load and recovery observations. Do not transplant these figures into another experiment without establishing that service’s baseline, customer-impact criteria, and acceptable recovery behavior. The guidance reviewed here does not establish a cross-industry statistic for chaos-experiment outcomes.
What to require from an experiment tool
Whether you use a managed service, a container-oriented framework, or a controlled script, evaluate the mechanism against the same safety capabilities.
| Capability | Questions to ask |
|---|---|
| Targeting | Can it select exact resources or labels and show the resolved target set before execution? |
| Scope and duration | Can it constrain count, percentage, region, fault duration, ramp-up, and backoff? |
| Steady-state integration | Can the experiment record the indicators and workload used to judge the hypothesis? |
| Stop behavior | Can monitored thresholds halt an active run and prevent new runs when necessary? |
| Rollback and recovery | Can it remove the condition, expose state, emit logs, and support a manual recovery path? |
| Rehearsal | Can the same template run safely in a lower-level environment before production? |
| State tracking | Does it provide a run ID and clear pending, active, stopped, failed, and completed states? |
| Environment fit | Does it work with the cloud, containers, hosts, network, and permissions your service actually uses? |
AWS identifies AWS FIS as an option for AWS workloads. Custom scripts require particular care: they need workload-state awareness, durable logging, rollback, and stop-condition capabilities. A script that can apply a fault but cannot prove its target or stop on customer impact is not a complete experiment control.
Run the preflight in this order
- Confirm the question and owner. Read the objective, hypothesis, target, fault, duration, and maximum blast radius aloud with the service owner.
- Check starting health. Verify every steady-state indicator, dependency, workload level, and active-change calendar item is within the approved range.
- Resolve and approve targets. Display the exact identifiers or labels, compare them with exclusions, and record the target set.
- Open the operator view. Confirm dashboards, logs, traces, synthetic checks, and alarm status are visible to the operator and backup.
- Test stop behavior. In the rehearsal environment, trip the relevant threshold and verify that the active run halts, the fault is removed or contained, and notifications arrive.
- Verify rollback access. Confirm the person on duty can reverse the action manually and knows the known-good recovery criteria.
- Notify the response group. Send the start window, expected signals, stop authority, escalation path, and planned end time.
- Start at the approved scope. Confirm the final configuration and run ID, then begin the injection while recording timestamps.
- Observe onset and recovery. Compare customer and component indicators with baseline; stop immediately when a guardrail requires it.
- Close and record. Remove the fault, verify recovery criteria, preserve evidence, and classify the result as supported, disproved, inconclusive, or invalid.
Common configuration failures and fixes
The target query selects the wrong resources
Symptom: the resolved count or environment label is unexpected. Fix: stop before injection, replace broad queries with explicit identifiers or tightly controlled labels, and add a pre-run target-count check.
The baseline is missing or the service starts unhealthy
Symptom: operators cannot tell whether an error began with the fault, or a deployment and the experiment overlap. Fix: postpone the run, restore a stable state, capture a fresh baseline, and reschedule outside the conflicting change window.
The Tool Desk
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Symptom: a threshold fires without stopping the experiment or reaching an accountable responder. Fix: connect the alarm to the supported stop mechanism, test the notification route, name a backup operator, and document the manual halt procedure.
Recovery is declared when only the target looks healthy
Symptom: the faulted instance is restored but customer errors, queues, or dependencies remain degraded. Fix: require service-level and user-facing recovery criteria, not only target-component status.
The experiment is too broad for the question
Symptom: several components or regions are affected, making causality unclear and increasing risk. Fix: reduce the target set, shorten duration, or split the question into separate experiments.
A custom script leaves no trustworthy record
Symptom: the team cannot prove what ran, where, or when it stopped. Fix: add durable run IDs, structured logs, explicit state transitions, rollback handling, and a final result record before using the script for higher-risk tests.
Final decision rule
Run the experiment only when the team can answer “what will we measure, what will make us stop, and how will we recover?” with concrete settings rather than assurances. A hypothesis alone does not make a run safe. Safety comes from bounded targeting, a verified baseline, complete observability, tested stop controls, a practiced rollback, clear ownership, and a risk-appropriate progression from rehearsal to production.
Principles of Chaos Engineering describes the discipline as “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.” The confidence comes from disciplined controls and evidence collected during the run, not from the injection itself.
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