All capability theses

A use case to evaluate

Choose a useful diagnostic check

With Jev, an agent can rank proposed tests against current failure evidence.

A human example

A service is unreachable even though its processes look healthy.

What the caller supplies

The agent proposes candidate causes and read-only tests, with a current state snapshot.

What happens next

Jev recommends a test or review. The agent runs the test, updates its hypothesis and verifies repair invariants.

Illustrative example, not a recorded result.

Potential value: high

Repeated incident and debugging work contains many choices about which evidence to collect next.

Evidence confidence: moderate

The SRE study measured more passing attempts overall, with regressions. Trace attribution also showed low joint diagnosis accuracy.

The rating describes support for this claim. It is separate from Jev's returned probability. How we assign ratings.

Evidence, including disagreement

The next test

This protocol is planned. Its outcome is not yet known.

30 reproducible local failures with known causes, misleading logs and cases where the correct hypothesis is omitted.

Compare against

  • Direct agent investigation
  • Fixed diagnostic checklist

Measure

  • Successful repairs
  • Diagnostic calls
  • Time to a supported cause
  • Durable repair tests

Decision after the test

Require better task outcomes or fewer diagnostic calls without more incorrect repairs. Report omitted-hypothesis failures separately.

The report will retain inputs, question versions, every attempt and failure examples. We will update the confidence rating after reviewing the result.

Use a related Engine recipe

Recipes are implementation starting points. Their presence does not mean the protocol above has passed.

Read or improve this thesis on GitHub.