DOCA.1.CHR:5 - Archetypal Grounding

DOCA.1.CHR:5.1 - Person and representative later Work

Mira’s bounded inquiry concerns incident-review and handoff Work over four months. “Become better at AI” is discarded as a characteristic. The selected characteristics are: time to identify a material handoff gap, quality of the explanation supplied to the next reviewer, ability to detect unsupported model output, confidential-data exposure, review workload, and transfer to two representative incident classes.

The current account has qualified examples for explanation quality and workload, but no comparable evidence for transfer or unsupported-output detection. Those readings remain unknown. The intended contribution is faster detection without worsening explanation quality, confidentiality, or sustainable workload. HCD owns the human demand, capability, support, and transfer evidence. The characterization returns those useful readings and unknowns; it supplies no course recommendation or transfer claim. Representative-Work evidence becomes a request only when the attainable contribution warrants acquisition for the next use.

DOCA.1.CHR:5.2 - Organization without one maturity score

An engineering organization is called “low maturity” because releases are slow. The inquiry concerns reliable product-family delivery over twelve months. The characterization separates release lead time, rework, service interruption, platform reuse, security exposure, scarce-specialist attention, team autonomy, and customer-impact distribution.

Lead time has a lower-is-better quantity scale under a stated window; team autonomy has an ordinal scale and no ratio arithmetic; security exposure uses separately governed risk results. The intended configuration may improve lead time while worsening specialist bottlenecks or security. The account therefore remains multi-coordinate and indeterminate until organization, platform, operations, and security owners provide the missing premises.

DOCA.1.CHR:5.3 - AI arrangement and configuration identity

A team wants to “improve the model.” The actual subject is a model edition with a retrieval corpus, tool permissions, evaluator, human escalation path, and operating environment. Selected characteristics include task success under the declared evaluation, false-action rate, explanation adequacy, data exposure, escalation latency, compute use, operator workload, and robustness to changed inputs.

A benchmark from the bare model does not characterize the deployed arrangement. Human learning evidence does not establish model adaptation. The next question is whether the current evaluation covers the intended tool-enabled configuration and protected safety/security conditions.

DOCA.1.CHR:5.4 - Already adequate characterization

A facility team already has current, source-qualified service, safety, operability, resource, and affected-community characteristics for the same configuration and horizon. No meaning, scale, or evidence window changed. The team reuses the account and proceeds to joint reachability. Reformatting it into this pattern’s compact form would add burden without changing action.