Human oversight procedure playbook (approval queues, escalation, overrides)
Download a human oversight procedure playbook covering approval queues, escalation, overrides, and evidence capture for AI agents.
Define a reviewable human oversight SOP in ~30 minutes.
For compliance, risk, product, and ML ops teams shipping agentic workflows into regulated environments.
Last updated: Dec 16, 2025 · Version v1.0 · Fictional sample. Not legal advice.
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What this artifact is (and when you need it)
Minimum viable explanation, written for audits — not for theory.
This playbook is a practical SOP for how humans supervise agentic workflows: what must be reviewed, how escalation works, and how overrides are justified.
It’s written to be auditable: every intervention produces an exportable record (who, what, when, why).
You need it when
- You are inserting approval gates into AI workflows (high-risk actions, sensitive data access, production changes).
- You need to prove who approved/overrode a decision and what context they saw.
- You are preparing a human oversight section for Annex IV or an internal control review.
Common failure mode
“Human in the loop” described in prose with no queue rules, no escalation SLAs, and no mandatory recorded rationale for overrides.
What good looks like
Acceptance criteria reviewers actually check.
- Roles and decision authority are explicit (requester, reviewer, approver, auditor).
- Queue rules define always-review vs sampled review vs auto-approve categories.
- Escalation ladder defines triggers, targets, and SLAs.
- Overrides require justification and (where applicable) attachments/ticket IDs.
- Stop/rollback procedure exists and is evidence-producing.
- Every intervention captures identity, timestamps, context shown, and policy version.
Template preview
A real excerpt in HTML so it’s indexable and reviewable.
## 2) Approval queue design (what gets reviewed) Always-review (blocking): - High-risk actions (money movement, account closure, eligibility decisions) - Policy violations / near-misses - Low-confidence or out-of-distribution cases ## 4) Override procedure (human can change the outcome) - Allowed override types (approve anyway, reject, edit output, stop workflow) - Required justification text (minimum content) - Two-person rule conditions (when required)
How to fill it in (fast)
Inputs you need, time to complete, and a miniature worked example.
Inputs you need
- List of high-risk actions and sensitive operations your agent can perform.
- Roles and decision authority (who can approve/override/stop).
- Escalation ladder + SLAs.
- Minimum evidence fields required for every intervention.
Time to complete: 20–40 minutes for v1, then iterate with real review logs.
Mini example: override rule
Override rule: - Allowed only for Approver role - Requires rationale (>= 2 sentences) + linked ticket ID - Two-person rule for: account closure, SAR filing recommendation, money movement
How KLA generates it (Govern / Measure / Prove)
Tie the artifact to product primitives so it converts.
Govern
- Policy-as-code checkpoints that block or require review for high-risk actions.
- Versioned change control for model/prompt/policy/workflow updates.
Measure
- Risk-tiered sampling reviews (baseline + burst during incidents or after changes).
- Near-miss tracking (blocked / nearly blocked steps) as a measurable control signal.
Prove
- Hash-chained, append-only audit ledger with 7+ year retention language where required.
- Evidence Room export bundles (manifest + checksums) so auditors can verify independently.
FAQs
Written to win snippet-style answers.