Multi-Agent Expert Review Workflow for AI Outputs

Use 3-5 sub-agents to review Claude's work from completely different angles simultaneously. Build expert profiles using real frameworks: Option 1: Use well-known figures whose thinking is in training data (e.g., Karpathy-style agent to tear apart prompts, Ogilvy's principles to roast copy). Option 2: Use NotebookLM to extract an expert card from a trusted YouTube channel, blog, newsletter, or podcast transcripts, capturing frameworks, decision patterns, and principles. Upload the expert material into OpenClaw memory or a Claude project. Process: 1. Claude generates the first draft. 2. Sub-agent 1 reviews through Expert A's lens. 3. Sub-agent 2 stress-tests with Expert B's framework. 4. Sub-agent 3 catches what others missed. 5. All run simultaneously. 6. Claude processes all feedback and rebuilds. 7. You only see the final version that survived 3-5 rounds of scrutiny.