AI systems foundations · Lesson 13 of 15 · 7 min

Use evidence before action

Separate observations from hypotheses. A correlation with a deployment is a useful lead, but logs, traces and a controlled comparison are needed before claiming cause.

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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-systems-foundations-013.json. Separate the recorded facts from your assumptions.
Hint & worked example
Separate observations from hypotheses. A correlation with a deployment is a useful lead, but logs, traces and a controlled comparison are needed before claiming cause.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-systems-foundations-013.json
```
Common mistake to avoid: Timing alone proves the deployment caused the latency increase.
2
Isolate the deciding signal
Select the causality_proven field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Treat the deployment as a hypothesis and compare evidence before declaring root cause.
A successful JSON query isolates causality_proven.
```
jq .causality_proven /opt/ai-lab/cases/aiops-systems-foundations-013.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: test-hypothesis, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-systems-foundations-013-decision.txt.
Hint & worked example
Treat the deployment as a hypothesis and compare evidence before declaring root cause.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'test-hypothesis' > /home/learner/lab/aiops-systems-foundations-013-decision.txt
```
Common mistake to avoid: Timing alone proves the deployment caused the latency increase.

Understand the pattern

Separate observations from hypotheses. A correlation with a deployment is a useful lead, but logs, traces and a controlled comparison are needed before claiming cause.

Interpret the evidence

Treat the deployment as a hypothesis and compare evidence before declaring root cause.

A tempting mistake

Timing alone proves the deployment caused the latency increase.

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-systems-foundations-013.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "latency_before_ms": 200,
  "latency_after_ms": 420,
  "deployment_nearby": true,
  "causality_proven": false
}

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

$ jq .causality_proven /opt/ai-lab/cases/aiops-systems-foundations-013.json

false

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

$ printf '%s\n' 'test-hypothesis' > /home/learner/lab/aiops-systems-foundations-013-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 “test-hypothesis” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Use evidence before action” justify?Timing alone proves the deployment caused the latency increase.Make the change now and gather the missing evidence afterward.Treat the deployment as a hypothesis and compare evidence before declaring root cause.

Apply it elsewhere

A colleague concludes: “Timing alone proves the deployment caused the latency increase.” What should you do?Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Write a system baseline Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision