Cost, capacity, and reliability · Lesson 9 of 14 · 7 min

Review fallback routing

Fallback routing needs explicit eligibility and limits. A fallback must preserve task, privacy and compatibility boundaries rather than serving any response at any cost.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-cost-reliability-009.json. Separate the recorded facts from your assumptions.
Hint & worked example
Fallback routing needs explicit eligibility and limits. A fallback must preserve task, privacy and compatibility boundaries rather than serving any response at any cost.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-cost-reliability-009.json
```
Common mistake to avoid: Any available fallback is acceptable during an outage.
2
Isolate the deciding signal
Select the fallback_privacy_approved field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Do not route sensitive work to the unapproved fallback.
A successful JSON query isolates fallback_privacy_approved.
```
jq .fallback_privacy_approved /opt/ai-lab/cases/aiops-cost-reliability-009.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: withhold-unapproved-fallback, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-cost-reliability-009-decision.txt.
Hint & worked example
Do not route sensitive work to the unapproved fallback.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'withhold-unapproved-fallback' > /home/learner/lab/aiops-cost-reliability-009-decision.txt
```
Common mistake to avoid: Any available fallback is acceptable during an outage.

Understand the pattern

Fallback routing needs explicit eligibility and limits. A fallback must preserve task, privacy and compatibility boundaries rather than serving any response at any cost.

Interpret the evidence

Do not route sensitive work to the unapproved fallback.

A tempting mistake

Any available fallback is acceptable during an outage.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-cost-reliability-009.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "primary_unavailable": true,
  "fallback_quality_pass": true,
  "fallback_privacy_approved": false
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .fallback_privacy_approved /opt/ai-lab/cases/aiops-cost-reliability-009.json

false

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'withhold-unapproved-fallback' > /home/learner/lab/aiops-cost-reliability-009-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “withhold-unapproved-fallback” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Review fallback routing” justify?Make the change now and gather the missing evidence afterward.Do not route sensitive work to the unapproved fallback.Any available fallback is acceptable during an outage.

Apply it elsewhere

A colleague concludes: “Any available fallback is acceptable during an outage.” What should you do?Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.Treat this fixture as a live measurement.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Plan graceful degradation Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision