Human Review Proportional to Risk
Which steps can be automated safely, and where must the agent stop and hand control to a person? Set review and approval thresholds from the consequence of an error instead of reviewing every output equally or granting blanket autonomy.
What You Will Be Able to Decide
- Explain human review proportional to risk in product and business terms.
- Apply this decision: Set review and approval thresholds from the consequence of an error instead of reviewing every output equally or granting blanket autonomy.
- Recognise this material risk: low-value review creates fatigue while a high-impact exception passes without accountable approval.
- Use this review: Run the triage workflow with missing data, an unsafe request, an unavailable tool, and an item that needs escalation.
A founder or operator is deciding how an AI agent should participate in a real workflow without inheriting undefined authority. This lesson gives you a concrete question to take into a build brief, proposal review, or product decision.
Which steps can be automated safely, and where must the agent stop and hand control to a person? The course example is An agent that prepares a weekly customer support triage queue; use it to decide what evidence would justify the choice before a builder implements it.
What Does Human Review Proportional to Risk Mean for Your Product?
A founder or operator is deciding how an AI agent should participate in a real workflow without inheriting undefined authority.
Use the illustrative service for this course (An agent that prepares a weekly customer support triage queue) to make the choice concrete. Which steps can be automated safely, and where must the agent stop and hand control to a person?
Technical term
Human Review Proportional to Risk
Proportional review increases human oversight as consequence, irreversibility, uncertainty, or sensitivity increases.
How Should a Founder Use Human Review Proportional to Risk?
For an agent that prepares a weekly customer support triage queue, ask what would happen if low-value review creates fatigue while a high-impact exception passes without accountable approval.
For this decision, the useful standard is that the agent behaves predictably across representative work, respects its boundaries, and produces evidence a responsible person can review.
- Decision: Set review and approval thresholds from the consequence of an error instead of reviewing every output equally or granting blanket autonomy.
- Evidence to request: show that the agent behaves predictably across representative work, respects its boundaries, and produces evidence a responsible person can review.
- Owner: name who will respond if low-value review creates fatigue while a high-impact exception passes without accountable approval.
- Record the result in the agent workflow specification, evaluation set, and operating record.
- Practical review: Run the triage workflow with missing data, an unsafe request, an unavailable tool, and an item that needs escalation.
How Do You Choose an Approach to Human Review Proportional to Risk?
Which steps can be automated safely, and where must the agent stop and hand control to a person? Set review and approval thresholds from the consequence of an error instead of reviewing every output equally or granting blanket autonomy.
The risk is that low-value review creates fatigue while a high-impact exception passes without accountable approval. Compare a simpler option with the proposed one, including who will operate either choice.
- Describe the user or business outcome that must be protected.
- Identify the most credible failure and its consequence.
- Compare the simplest adequate approach with one realistic alternative.
- Set a review point for when the decision may need to change.
What Evidence Should You Accept for Human Review Proportional to Risk?
What Warning Signs Should You Look For?
- The proposal does not address this risk: low-value review creates fatigue while a high-impact exception passes without accountable approval.
- Nobody can show whether the agent behaves predictably across representative work, respects its boundaries, and produces evidence a responsible person can review.
- The decision has no named owner or review point.
What Should You Ask a Consultant?
- What changes for the user if we choose this approach to human review proportional to risk?
- How have we reduced or accepted this risk: low-value review creates fatigue while a high-impact exception passes without accountable approval.
- Can you demonstrate that the agent behaves predictably across representative work, respects its boundaries, and produces evidence a responsible person can review?
- Who owns the result, and when will we reconsider it?
Key takeaway
Key Takeaway
Set review and approval thresholds from the consequence of an error instead of reviewing every output equally or granting blanket autonomy. Ask for evidence against the specific risk: low-value review creates fatigue while a high-impact exception passes without accountable approval.