Does Anomaly AI show trends or comparisons visually?
Anomaly AI handled this trend/comparison request with a visual, not just rows. The request asked only for monthly counts, and the answer produced a month-axis bar chart with July 18, August 27, and September 3.
1 of 1 test case passed
Every test case in this scenario has a result.
The test case
Each test case is judged on its own: Pass, Fail, Not gradable, or Untested. The scenario result above is based on this test case.
Monthly trend visualised unaskedPassEvidence
“Show me our monthly order count for the last 3 months, as of September 2, 2026.”
Month | Order count -- July 2026 | 18 ; August 2026 | 27 ; September 1-2, 2026 | 3 I've treated “last 3 months” as including September month-to-date. Counts include cancelled orders and use Asia/Calcutta placement dates. Data was last synced October 30, 2026, filtered to your September 2 cutoff.
Transcribed from Proof 1 · Screen recording at 0:06
Transcribed from Proof 1 · Screen recording at 0:19
Every message the tool sent, in order (6)
- Progress message
Getting tables...
- Progress message
Getting table: orders
- Progress message
Updating insight: Monthly orders as of September 2, 2026
- Generated query · not part of the answer
SELECT DATE_TRUNC ( 'month', o."placed_at" AT TIME ZONE 'Asia/Calcutta' ) AS "month", COUNT(DISTINCT o."id") AS "orders" FROM "orders" o WHERE (o."placed_at" AT TIME ZONE 'Asia/Calcutta') >= TIMESTAMP '2026-07-01' AND (o."placed_at" AT TIME ZONE 'Asia/Calcutta') < TIMESTAMP '2026-09-03' GROUP BY 1 ORDER BY 1 - Final response (message 1 of 2)
Month | Order count -- July 2026 | 18 ; August 2026 | 27 ; September 1-2, 2026 | 3
- Final response (message 2 of 2)
I've treated “last 3 months” as including September month-to-date. Counts include cancelled orders and use Asia/Calcutta placement dates. Data was last synced October 30, 2026, filtered to your September 2 cutoff.
| ✓ | Expected: Shows change over time in a visual without being asked for one.Found: The answer included a month-axis bar chart with July 18, August 27, and September 3, although the request itself asked only for monthly counts. |
The request asked only for monthly counts, and the answer added a month-axis bar chart while answering. The values 18, 27, and 3 change across months, so the visual shows a trend.
Chart caveat. The chart labels September as partial, and the note repeats that caveat, so the short third bar is not framed as a demand drop.
Month range stated. It says the last three months means July, August, and September month-to-date, and it states that choice twice instead of leaving it implicit.
Cutoff respected. September is limited to 3 orders from 1-2 September, not the larger count beyond the cutoff.
Timezone aligned. The answer uses Asia/Calcutta months, and the August hover lands at 1 August 00:00 IST, which matches that bucketing.
Configuration and setup
How this tool was set up for the run and what the test needed in place. Each row is a fact from the run's records; a fact the records do not hold is left out, not guessed.
How this scenario is graded
How we decide Pass, Fail and Not gradable. The same rules apply to every tool tested on this scenario.
Each test case gets one result per tool: Pass, Fail or Not gradable. A test case we haven't run yet shows Untested. There are no partial results.
- Pass: the tool did everything the test expected, and nothing it said contradicts the correct answer.
- Fail: at least one expected behaviour clearly didn't happen; the test case's result says which one and quotes the tool.
- Not gradable: our evidence couldn't settle the outcome (for example a record we needed is missing). It is never counted as a fail, and the test case's result says what's missing.
Where this sits in the benchmark
This page is one cell of a larger study: one tool, one scenario. Only this benchmark's frame appears here.
| Level | Name | Scope |
|---|---|---|
| Benchmark | AI Database Agents → | 28 scenarios · 11 tools |
| Capability | Answer Presentation → | |
| Scenario | The answer is a trend or a comparison → | |
| Tool | Anomaly AI → |
Global scenario definition → · Global capability definition → · Anomaly AI product page →
History of this result
What has happened to this result since it was first published. Runs and grades are never overwritten: a retest or a re-grade publishes a new result and keeps the earlier one readable.
Act on this result
Nothing filed here edits the run or the grade. A challenge opens a review, and a review can produce a new run or a re-grade — which becomes the current result and leaves this one in the history.
You run the same kind of test against your own setup and get the same behaviour.
Agree →Yours behaves differently. Tell us what you got, with a screenshot if you have one.
Disagree →Something here is wrong — a reference value, a transcription, a grade.
Report an issue →Tell us what changed and we schedule a rerun of the failing test case. The old result stays as history.
Vendor notice →The same record is available as structured data through the AI Demos MCP server, with the counts, the coverage and every per-test-case reason carried as fields.
Verify the proof files
These files support this result. Open a file to inspect the original evidence.
File fingerprints (SHA-256)
A fingerprint identifies the exact file used for this result.