# Meeting actions: retain only agreed outstanding tasks

Status: real API trials recorded on 11 September 2026 (Brisbane); provisional report awaiting human review and the baseline decision. Nothing has been published.

## Question and result

Can Grok extract agreed actions without inventing owners or resolving explicitly ambiguous dates?

3 of 3 pipeline attempts returned completed model output. Those outputs matched 36/36 case references. Across all attempted pipeline cases, 36/36 returned a correct answer; missing outputs remain failures of the pipeline and are not scored as model answer errors. All completed outputs matched the exact prompt and finished normally.

The reference has ten outstanding tasks across 12 cases. Checks include cancellation, suggestions without agreement, reassignment, deadline correction, missing owners and dates, tomorrow relative to a dated meeting, an ambiguous Friday, completed work, duplicate mentions and a quoted instruction to invent a task.

## Method and evidence

Fixed [inputs](../meeting-actions/inputs.json), [prompt](../meeting-actions/prompt.txt), [authored reference](../meeting-actions/expected.json), [manifest](../meeting-actions/manifest.json) and [protocol](../meeting-actions/PROTOCOL.md) were prepared before runs. The [dated run-plan amendment](../API-RUN-PLAN-2026-09-11.md) records the missing human baseline and infrastructure repair. Raw records were exported directly from the private dashboard’s D1 database; corresponding .score.json files and [summary](../results/2026-09-11/summary.json) are retained.

Each run requested x-ai/grok-4.3 through OpenRouter, temperature 0, low reasoning, maximum 4,000 output tokens, JSON output, no web/X search and no automatic fallback. Each trial used one fresh request with the same prompt and no answer key. Inspect the raw record for reported model/provider, generation ID, usage and settings. The trials repeat fixed cases and are not an independent sample of business documents.

| Attempt | Raw record | Outcome | Exact cases | Model latency | Reported API cost |
|---|---|---|---|---|---|
| 1 | [20a43b08](../results/2026-09-11/20a43b08-0a87-468b-9a61-a581c7b01dac.json) | completed | 12/12 | 9.020 s | US$0.00453340 |
| 2 | [a9d8bfd1](../results/2026-09-11/a9d8bfd1-2928-4776-98f4-08b64f7bfef9.json) | completed | 12/12 | 8.727 s | US$0.00361260 |
| 3 | [e997c7ad](../results/2026-09-11/e997c7ad-9250-4c9b-b354-148748e7b4c3.json) | completed | 12/12 | 8.792 s | US$0.00365260 |

## Time and cost

Completed model-request times ranged from 8.727–9.020 seconds. Provider-reported costs total US$0.01179860. A further US$0.00 remains reserved with no confirmed invoice cost. Provider reporting is not invoice reconciliation; the app rounds allocations upward to microdollars.

Human setup, checking, correction and baseline times were not measured. Hosting, development effort and subscription overhead have not been allocated. This is not total delivery cost, a speedup result or a profit calculation. Revenue and attributable customer interest are unmeasured.

## Limitations and decision

The same 12 short synthetic transcripts were repeated. Bracketed task IDs deliberately simplify matching. No audio, speech recognition, real meeting, speaker identification, long transcript or production task-creation system was tested. Task ordering is ignored by the scorer; owners and dates must match exactly, including null values.

The assistant reviewed references and outputs; independent human checking is still pending. The current evidence supports only the observed behavior on these fixtures. Do not infer a population-level accuracy guarantee from repeated tiny examples.

A team-specific action-extraction assessment would need permissioned notes and owner review before any tasks are created. These results do not establish production reliability or time savings.

## Sources and reproduction

[Microsoft’s meeting recap documentation](https://support.microsoft.com/en-us/teams/meetings/recap-in-microsoft-teams) supplies workflow/model context, not evidence for our trial scores or commercial demand. [OpenRouter usage accounting](https://openrouter.ai/docs/cookbook/administration/usage-accounting) describes the reported cost field. Our findings come from the linked raw run records.

From the project directory, run `node scripts/score-workflow-trial.mjs meeting-actions experiments/results/2026-09-11/RUN-ID.json` for each record. Use `node scripts/score-pilot-evidence.mjs` and `node scripts/build-pilot-reports.mjs` to regenerate scores and these reports without making provider calls.