Post-market Monitoring Plan Template (with sampling policy)
Download a post-market monitoring plan template with a risk-tiered sampling policy section for AI agents and regulated workflows.
Generate a reviewable post-market monitoring plan in 30–60 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.
Report an issue: /contact
What this artifact is (and when you need it)
Minimum viable explanation, written for audits — not for theory.
A post-market monitoring plan is the operational document that defines what you monitor after deployment, what thresholds matter, and what happens when thresholds are breached.
Auditors want to see owners, sampling rates, reviewer guidance, and an incident workflow — not a paragraph that says “we will monitor”.
You need it when
- You are deploying an agent into a regulated workflow (credit, claims, KYC/AML, HR).
- You need to prove ongoing quality, safety, and policy compliance after go-live.
- You are preparing an Annex IV dossier or an audit readiness review.
Common failure mode
A plan that lists metrics but has no thresholds, no risk-tiered sampling policy, and no incident workflow (who does what, by when).
What good looks like
Acceptance criteria reviewers actually check.
- Signals cover quality, policy compliance, tool/action correctness, and operational health.
- Each signal has thresholds, severity levels, and a named owner + response SLA.
- Sampling policy defines what is sampled, at what rates, and how reviewers label outcomes.
- Incident process exists (severity, rollback/kill switch, reporting, corrective actions).
- Material change definition triggers re-validation and evidence refresh.
- Evidence can be exported as a verifiable bundle (manifest + checksums).
Template preview
A real excerpt in HTML so it’s indexable and reviewable.
# Post-market Monitoring Plan Template (with sampling policy) ## 2) Monitoring signals (what you measure) - Quality: grounding/accuracy sampling, format validity - Safety & policy: violations (blocked) + near-misses - Tool/action correctness: wrong system/action/record - Operational health: latency, throughput, cost, error rate ## 4) Sampling policy (risk-tiered, auditable) - What gets sampled (risk tiers + always-sample rules) - Sampling rates (baseline + burst during incidents) - Reviewer guidance (labels/rubrics + disagreement handling)
How to fill it in (fast)
Inputs you need, time to complete, and a miniature worked example.
Inputs you need
- Risk tiers (low/medium/high) and which actions fall into each tier.
- Monitored signals + thresholds + owners.
- Sampling rates (baseline + burst rules) and reviewer rubric.
- Incident roles, response SLAs, and rollback procedure.
- Definition of “material change” and re-validation expectations.
Time to complete: 30–60 minutes for a defensible v1.
Mini example: sampling policy (risk-tiered)
Risk tiers: - High: money movement, account closure, eligibility decisions - Medium: recommendations that influence a human decision - Low: low-stakes automation with strong guardrails Sampling rates: - High: 25% baseline; 100% during incidents or after major changes - Medium: 5% baseline; 20% for 2 weeks after new model/policy versions - Low: 1% baseline
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.