Incident response and capstones · Lesson 1 of 12 · 8 min

Triage elevated latency

Latency triage starts with a scoped symptom, then traces its largest contributor. Separate the evidence from the hypothesis and prefer a reversible next action.

Open the lab Course map

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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-incidents-capstones-001.json. Separate the recorded facts from your assumptions.
Hint & worked example
Latency triage starts with a scoped symptom, then traces its largest contributor. Separate the evidence from the hypothesis and prefer a reversible next action.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-incidents-capstones-001.json
```
Common mistake to avoid: Raise GPU limits without checking the dominant span.
2
Isolate the deciding signal
Select the dominant_span field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Investigate retrieval before changing the normally loaded runtime.
A successful JSON query isolates dominant_span.
```
jq .dominant_span /opt/ai-lab/cases/aiops-incidents-capstones-001.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: triage-retrieval, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-incidents-capstones-001-decision.txt.
Hint & worked example
Investigate retrieval before changing the normally loaded runtime.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'triage-retrieval' > /home/learner/lab/aiops-incidents-capstones-001-decision.txt
```
Common mistake to avoid: Raise GPU limits without checking the dominant span.

Understand the pattern

Latency triage starts with a scoped symptom, then traces its largest contributor. Separate the evidence from the hypothesis and prefer a reversible next action.

Interpret the evidence

Investigate retrieval before changing the normally loaded runtime.

A tempting mistake

Raise GPU limits without checking the dominant span.

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-incidents-capstones-001.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "p95_ms": 1400,
  "target_ms": 800,
  "dominant_span": "retrieval",
  "runtime_load": "normal"
}

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

$ jq .dominant_span /opt/ai-lab/cases/aiops-incidents-capstones-001.json

"retrieval"

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

$ printf '%s\n' 'triage-retrieval' > /home/learner/lab/aiops-incidents-capstones-001-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 “triage-retrieval” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Triage elevated latency” justify?Investigate retrieval before changing the normally loaded runtime.Raise GPU limits without checking the dominant span.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “Raise GPU limits without checking the dominant span.” 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: Investigate inference errors Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision