Scenario in benchmark · Version 1

The user wants several findings in one place

A user asks to combine several distinct findings into one report-like artifact.

How the tools performed

Every in-scope tool is visible. Outcomes come from current published Results for this scenario and benchmark version.

Publication availability: 3 tools have a current published Result.

No published result does not tell you whether a tool has been tested. Test coverage is shown only for comparable published Results.

ToolPublished outcomesTest coverageResult
Published Results · alphabetical, not ranked
AskYourDatabase
1 Pass0 Fail0 Not gradable
1 of 1 test case passed
1 of 1 assessed1 of 1 gradableView Result →
BlazeSQL
1 Pass0 Fail0 Not gradable
1 of 1 test case passed
1 of 1 assessed1 of 1 gradableView Result →
FutureSmart Database Agent
0 Pass1 Fail0 Not gradable
1 of 1 test case failed
1 of 1 assessed1 of 1 gradableView Result →
No published result · alphabetical
AI for Database——No published result
Anomaly AI——No published result
Basedash——No published result
camelAI——No published result
Definite——No published result
Dot——No published result
Draxlr——No published result
Querio——No published result

Assessed = Pass + Fail + Not gradable. Gradable = Pass + Fail. Both use the published Result’s pinned-test denominator. — means not publicly available.

Test design

Pinned test cases
1
Disclosure
0 public · 1 withheld
Capabilities exercised here
Reporting

What this scenario evaluates

  • Whether the agent combines several distinct findings into one coherent artifact.
  • Whether each included finding still matches the answer to its own original question.
  • Whether the combined output keeps the findings consistent with one another rather than mixing, dropping, or altering them.
  • Whether the result remains a report-like summary of already produced answers, not a fresh analysis with changed meaning.

Exact wording, inputs, fixture state, expected output and detailed grading remain at the test-case level and may be withheld while the benchmark version is active. The scenario and its evaluation intent are public.

How the results are graded

  • Pass: the test-case expectations hold.
  • Fail: an expectation demonstrably does not hold.
  • Not gradable: the evidence cannot establish the outcome.

Version 1 uses test-case expectations; no scenario rubric is pinned.

Benchmark methodology →
The user wants several findings in one place in AI Database Agents | AI Demos