AI Agent Quality Control Framework

You are an AI operations expert who specializes in human-AI collaboration for marketing teams. Help me build a quality control framework for supervising AI agents that execute marketing tasks. **My AI Agent Setup:** - AI agents I use: [DESCRIBE YOUR AGENTS - E.G., CONTENT WRITER, EMAIL SENDER, AD OPTIMIZER] - Tasks they execute autonomously: [LIST AUTOMATED TASKS] - Current QA process: [HOW YOU CHECK THEIR WORK NOW - OR "NONE"] - Past failures or near-misses: [ANY AI ERRORS THAT CAUSED PROBLEMS] **Build my AI supervision framework:** 1. **Risk Classification Matrix:** - Categorize all AI agent tasks by risk level (low/medium/high/critical) - Which outputs need human review before publishing? - Which can run fully autonomous? - Decision tree for edge cases 2. **Pre-Flight Checklists:** - Input validation: What to verify before AI runs - Context requirements: What info agents need to avoid errors - Constraint definitions: Non-negotiables the AI must follow 3. **Output QA Protocols:** - Spot-check sampling strategy (what % to review, how to select) - Red flag patterns that trigger full review - Brand voice consistency checks - Factual accuracy verification methods - Compliance and legal review triggers 4. **Feedback Loop System:** - How to log and categorize AI errors - Process for updating agent instructions based on failures - Performance metrics to track agent quality over time - Escalation procedures for critical failures 5. **Human-in-the-Loop Touchpoints:** - Where humans add value AI cannot - Approval workflows for high-stakes outputs - Training data collection from corrections 6. **Emergency Protocols:** - How to pause/kill an agent causing problems - Communication templates for AI-caused errors - Recovery procedures for published mistakes Output a one-page QA checklist I can use immediately plus a full framework document.