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Human Approval Boundaries

Define which AI-assisted suggestions require accountable human review before becoming standards.

Human Approval Boundaries

Definition

Human approval boundaries separate preparation from governed correctness evidence. A suggestion, reviewer comment, interview answer, or draft standard can inform the loop, but it should not govern benchmark interpretation until the relevant human approval state is clear.

Use this page when prepared or contributed material is about to become a governed Policy, Rubric, Case-scoped reference output, or selected Benchmark Dataset evidence.

Comparison Directions are a narrower configuration object, not a governed standard. An AI-suggested Comparison Direction can be active without a separate approval step, but generated Cases, Benchmark membership, Policies, Rubrics, and reference outputs still follow their owning review or approval boundaries. A customer-owned human review packet is assembled from evidence; it is not a Teammately approval state.

Fields, states, or lifecycle rules

  • Draft suggestions and reviewer notes are preparation material.
  • Approved Policies and Rubrics, reviewed Case changes, selected Dataset membership, and supported Case-scoped reference outputs can affect governed evidence.
  • AI-suggested Comparison Directions can affect future variant generation as active directions, but they do not approve the generated cases or standards they help explore.
  • Approval should name the artifact being approved, not only the discussion that produced it.
  • Stale or superseded approvals should be visible before older benchmark evidence is reused.
  • This page does not claim external compliance approval, legal signoff, or production deployment authorization.

Read this with What AI Features Can and Cannot Do, Comparison Directions, Approving Suggested Policies, Editing Suggested Rubrics, and Approval History and Reviewer Activity.

Worked example

Human Approval Boundaries boundary

01

Start

Behavior input

Reviewer context
Several experts reject unsupported refund exceptions.
02

Middle

Judgment into standard

Suggested policy
The system proposes a policy that exceptions require approved support.
Approval boundary
The suggestion becomes governed only when a human owner approves the policy and its applicability.
03

Result

Benchmark output

Benchmark interpretation
Runs should cite the approved policy, not the unapproved suggestion that preceded it.

Source confidence

Code-backed: Policy approval and activity components expose accountable approval state and history; contributed-artifact types keep expert learning distinct from materialized governed objects. Comparison Directions deliberately use active, archived, and advisory-stale behavior instead of the Policy approval lifecycle.

Related task pages

Reference / Governance and ReproducibilityWhat AI Features Can and Cannot DoExplain the difference between AI-assisted suggestions and approved correctness infrastructure.Reference / AssetsComparison DirectionsCreate reusable guidance for meaningful candidate-output differences in comparative expert work.Reference / Correctness GovernancePolicies and RubricsUnderstand the governed relationship between behavior policies, applicability, binary rubrics, linked cases, and expert provenance.Quickstart / Learning ManualProduct quickstartConfigure one project foundation, one benchmark slice, one expert contribution, one evaluation, and one evidence-backed improvement.Concept / Operating ManualTask indexRoute correctness work to the current project foundation, benchmark workspace, evaluation, or improvement surface.

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Related docs

Governance and Reproducibility What AI Features Can and Cannot Do Explain the difference between AI-assisted suggestions and approved correctness infrastructure. Assets Comparison Directions Create reusable guidance for meaningful candidate-output differences in comparative expert work. Expert Contributions Contributed Artifacts Inspect policies, rubrics, cases, and coverage observations produced through attributable expert contribution work.
AI context