Cost, capacity, and reliability · Lesson 10 of 14 · 8 min

Plan graceful degradation

Graceful degradation retains a safe useful subset when dependencies fail. Make the reduced behavior explicit rather than pretending the full service is healthy.

Open the lab Course map

Mastery 0/3Run itExplain itApply it

learner@clairos:~ $

↑ history · Tab complete · Ctrl+C cancel · Esc, Tab to leaveExit 0

Your objectives

Hints are always available. Wrong turns never cost XP.

1
Read the case evidence
Read /opt/ai-lab/cases/aiops-cost-reliability-010.json. Separate the recorded facts from your assumptions.
Hint & worked example
Graceful degradation retains a safe useful subset when dependencies fail. Make the reduced behavior explicit rather than pretending the full service is healthy.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-cost-reliability-010.json
```
Common mistake to avoid: Hide the retrieval outage and generate a confident answer anyway.
2
Isolate the deciding signal
Select the safe_mode field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Use the explicit source-unavailable path rather than inventing grounded evidence.
A successful JSON query isolates safe_mode.
```
jq .safe_mode /opt/ai-lab/cases/aiops-cost-reliability-010.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: degrade-explicitly, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-cost-reliability-010-decision.txt.
Hint & worked example
Use the explicit source-unavailable path rather than inventing grounded evidence.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'degrade-explicitly' > /home/learner/lab/aiops-cost-reliability-010-decision.txt
```
Common mistake to avoid: Hide the retrieval outage and generate a confident answer anyway.

Understand the pattern

Graceful degradation retains a safe useful subset when dependencies fail. Make the reduced behavior explicit rather than pretending the full service is healthy.

Interpret the evidence

Use the explicit source-unavailable path rather than inventing grounded evidence.

A tempting mistake

Hide the retrieval outage and generate a confident answer anyway.

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-010.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "retrieval_available": false,
  "safe_mode": "show source-unavailable notice",
  "invent_answer": false
}

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

$ jq .safe_mode /opt/ai-lab/cases/aiops-cost-reliability-010.json

"show source-unavailable notice"

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

$ printf '%s\n' 'degrade-explicitly' > /home/learner/lab/aiops-cost-reliability-010-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 “degrade-explicitly” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Plan graceful degradation” justify?Use the explicit source-unavailable path rather than inventing grounded evidence.Hide the retrieval outage and generate a confident answer anyway.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “Hide the retrieval outage and generate a confident answer anyway.” What should you do?Accept it because the training command completed.Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Read reliability targets Try an incident drill

Anonymous lesson and mastery progress lasts for this session. Sign in to keep synced lesson XP between sessions; mastery stays device-local. No speed bonus, lost lives, or hint penalties.

Keep learning

Case methodology: evidence, limits, decision