This is an evaluation proposal, not a tested Argon integration. Extraction produces a draft record; it does not authorize sending messages, creating calendar events or changing a project system.
Define the fields and unknowns
Number the source passages and decide which fields the receiving process actually needs. An unspecified owner or date should stay null. Discussion of a possible action should not become an assigned commitment. Preserve relative dates unless a supplied reference date makes conversion unambiguous.
Include notes with ambiguous responsibility and conflicting deadlines in your evaluation set. Those cases show whether the system recognizes missing information instead of inventing a complete-looking register.
A practical review sequence
- Provide source notes and the required output format.
- Use the extraction prompt to define the fields and constraints.
- Check every action against an explicit source commitment.
- Resolve unclear owners or deadlines with the responsible person.
- Import only the approved records into the actual workflow.
Measure usable records
Track missing actions, invented actions, incorrect owners, changed dates and valid source references. If you request JSON, parse it with your normal JSON parser as part of acceptance. A structurally valid file can still contain incorrect facts; check both structure and content.
For a batch pilot, measure correction time and cost per approved record. Our 100-request extraction example is an arithmetic budget, not measured usage. Published coding benchmarks in the Astra comparison help illustrate why performance varies by task; they do not establish extraction quality.
Automation evidence is a starting point
Google's model table reports Argon at 51.3% on AutomationBench. That result supports evaluating business automation, but does not establish accuracy on your meeting notes or the behavior of a particular connected application. Published table · Workflow benchmark context.