AI agents

Sampling, calibration, and reviewer load

• How Many Outputs to Label: Sizing a Human Review Sample
• Recording Rater Scores Without Rater Names
• Sample Rates by Risk Tier: Proving the Audit Was Met
• Sampling Approvals: How Often to Review Agent Output by Task Risk
• Sampling Subagent Instructions: When a Delegation Needs Review
• Sizing a Review Sample: Confidence, Error Rate and Volume
• Turning a Review Sample Into a Stop or Go Signal
• A Decision Rule Template: Threshold, Confidence, Cost
• A Review Dashboard: Sample Size, Coverage and Cost per Run
• A Reviewer Workload Metric: Load, Backlog and Time-to-Decision
• A Sampling Policy Schema: Versioning Rates and Owners
• After a Rejected Sample: Closing the Review Feedback Loop
• Approval Queue Latency: Instrumenting Time-to-Decision by Risk
• Audit-Ready Sample Logs: Fields That Prove the Review Happened
• Calibrating Rater Agreement Against a Gold Set
• Choosing a Review Sample Size When Failures Are Rare
• Confidence Intervals for a Review Sample
• Estimating the Weekly Cost of a Review Sample
• From Sizing to Spend: Budgeting Raters and Cases per Quarter
• Hashing Rater IDs: Collision Risk and a Key Rotation Plan
• Picking Reviewers for AI Output: Skill, Calibration and Cost
• Reviewer Assignment Inside a Tier: Rotation, Calibration and Cost
• Reviewer Rotation: Spreading Oversight Without Losing Context
• Sampling Cited Answers: A Rate by Risk Tier
• Sampling Review Cases When Failures Are Rare
• Sampling Review Cases When Shadow Disagreements Are Rare
• Sampling Stratified by Risk: Allocating Reviews Across Tiers
• Sizing a Blind Review Sample From Expected Variance
• Sizing the Shadow Run: Cases and Raters to Trust the Score
• Spotting a Reviewer Who Stopped Reading
• Storing Rubric Versions Without Storing Rater Identity
• Stratified Confidence: One Coverage Number From Tier Counts
• Stratified Confidence: Turning Tier Counts Into Coverage
• Stratified Review Samples: Allocating Cases Across Risk Tiers
• Three Lanes: Judge, Human or Prompt Fix
• Weighting a Sample: Stratified Counts Into One Business Number
• When a Rater Asks for Their Own Scores: A Lookup Without a Map
• When Reviewer Labels Disagree: A Tie-Break and Adjudication Log