• Thesis: Using ELO for AI agent evaluation = measuring noise
• Problem: Wrong evaluators, wrong metrics, wrong assumptions
• Solution: Quantitative assessment frameworks

Chess ELO

• FIDE arbiters: 120hr training
• Binary outcome: win/loss
• Test-retest: r=0.95
• Cohen's κ=0.92

AI Agent ELO

• Random users: Google engineer? CS student? 10-year-old?
• Undefined dimensions: accuracy? style? speed?
• Test-retest: r=0.31 (coin flip)
• Cohen's κ=0.42

• Anchoring: 34% rating variance in first 3 seconds
• Confirmation: 78% selective attention to preferred features
• Dunning-Kruger: d=1.24 effect size
• Result: Circular preferences (A>B>C>A)

Objective Metrics

• McCabe complexity ≤20
• Test coverage ≥80%
• Big O notation comparison
• Self-admitted technical debt
• Reliability: r=0.91 vs r=0.42
• Effect size: d=2.18

Dream

• World's best engineers
• Annotated metrics
• Standardized criteria

Reality

• Random internet users
• No expertise verification
• Subjective preferences

1. Stop: Using preference votes as quality metrics
2. Start: Automated complexity analysis
3. ROI: 4.7 months to break even

• Kapoor et al. (2025): "AI agents that matter" - κ=0.42 finding
• Santos et al. (2022): Technical Debt Grading validation
• Regan & Haworth (2011): Chess arbiter reliability κ=0.92
• Chapman & Johnson (2002): 34% anchoring effect

"You can't rate chess with basketball fans"

"0.31 reliability? That's a coin flip with extra steps"

"Every preference vote is a data crime"

"The psychometrics are screaming"

• Technical Debt Grading (TDG) Framework
• PMAT (Pragmatic AI Labs MCP Agent Toolkit)
• McCabe Complexity Calculator
• Cohen's Kappa Calculator

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