Gate AI Workflow Acceptance on Mandatory Checks
An agent can configure most of a workflow correctly and still leave a required approval broken. If your evaluation reports only an average score, that failure can disappear inside an encouraging number. OpenAI's October 6 publication, Advancing computer use with Ironclad , makes this a concrete engineering problem. The researchers worked with practitioners to define 11 tasks across legal,…
The article discusses the limitations of using average scores to evaluate AI workflows, specifically with OpenAI's GPT-6 Astra and GPT-5.6 Sol models. While these models achieved mean rubric scores of 55.0% and 41.6% respectively, this does not necessarily indicate that the workflows were completed perfectly. The article also emphasizes the importance of separating progress scores from the conditions that allow a workflow to be used, and proposes a pattern for implementing mandatory checks.
This pattern involves defining versioned requirements, observable cases, and explicitly attaching these cases to a configuration version. The article concludes with a small Python function that can be used to verify the acceptance of mandatory checks, ensuring that only explicit passes with an evidence reference for the requested version are accepted.
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