Analyzing AI Experiments
Identify the experiment's core elements:
- Objective: What was the AI trying to achieve?
- Metrics: How was success/failure measured?
- Architecture: What tools and constraints were used?
- Results: What worked, what failed, and why?
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Extract Key Metrics
- Quantitative outcomes (revenue, accuracy, completion rates)
- Qualitative behaviors (decision patterns, failure modes)
- Timeline and progression data
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Analyze Architecture Changes
- Tool additions/removals and their impact
- Process modifications (workflows, constraints)
- Multi-agent interactions and dynamics
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Identify Success Factors
- What specific changes drove improvements?
- Which capabilities emerged or degraded?
- How did context/environment affect performance?
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Catalog Failure Modes
- Systematic weaknesses (gullibility, over-optimization)
- Edge cases and unexpected behaviors
- Security vulnerabilities or misalignment
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Extract Design Principles
- What worked across different conditions?
- Which assumptions were validated/invalidated?
- How do findings generalize beyond the specific experiment?
Progress:
- Document baseline vs. final performance
- Map architectural changes to outcomes
- Identify robust vs. brittle capabilities
- Note unexpected emergent behaviors
- Synthesize actionable insights
Example 1: Performance Analysis Input: "Phase 2 showed 80% reduction in discounts and 50% reduction in free items after CEO introduction" Output: "CEO oversight mechanism effectively constrained reward-hacking behavior, but approval rate suggests CEO shared same biases as original agent. Constraint mechanism worked; oversight quality was inadequate."
Example 2: Failure Mode Classification Input: "Agent agreed to fixed-price onion futures contract without understanding market risk" Output: "Demonstrates lack of domain knowledge transfer - agent has general reasoning but missing business-specific risk assessment. Suggests need for specialized training or expert system integration for domain-critical decisions."
For Experiment Analysis:
- Look for both intended and unintended consequences of changes
- Track how capabilities transfer (or fail to transfer) across domains
- Note the difference between performance in controlled vs. adversarial settings
- Identify which human oversight was effective vs. theatrical
For Architecture Assessment:
- Evaluate tool effectiveness by specific use cases, not general utility
- Assess whether multi-agent systems solve problems or create new failure modes
- Consider scalability and robustness, not just peak performance
- Map failure modes to specific architectural choices
For Insight Extraction:
- Distinguish between model capabilities and deployment readiness
- Identify which improvements came from better models vs. better systems
- Note how human behavior adapted to exploit or work with the AI
- Consider what this reveals about similar future deployments
- Survivorship bias: Only analyzing successful runs or ignoring subtle failures
- Overgeneralizing: Assuming findings apply beyond the specific experimental context
- Tool attribution error: Crediting performance gains to the wrong architectural changes
- Missing adversarial dynamics: Not accounting for how humans adapt their behavior
- Capability confusion: Mistaking task performance for general intelligence or readiness