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infinite-agents-public/infinite_variants/infinite_variant_5/test_output/PROFILE_COMPARISON.md

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Configuration Profile Comparison

This document shows how the same 5-iteration generation task would behave differently with each configuration profile.

Test Scenario

Task: Generate 5 D3.js visualizations Spec: specs/example_spec.md Output: test_output/

Development Profile (USED IN THIS TEST)

File: .claude/config/profiles/development.json

Key Settings

{
  "max_parallel_agents": 2,
  "batch_size": 3,
  "min_uniqueness_threshold": 0.7,
  "enable_review_stage": true,
  "enable_progressive_sophistication": false,
  "output_directory": "output_dev",
  "naming_pattern": "{theme}_dev_{iteration:03d}.html",
  "include_metadata": true,
  "web_enhancement.enabled": true,
  "web_enhancement.initial_priming_urls": 2,
  "web_enhancement.cache_web_content": true,
  "logging.level": "debug",
  "logging.verbose": true,
  "limits.max_iterations": 10,
  "limits.max_file_size_kb": 300,
  "chain_prompting.stages": 8
}

Behavior for 5 Iterations

  • Execution: 2 waves (Wave 1: 3 iterations, Wave 2: 2 iterations)
  • Parallel agents: 2 per wave
  • Quality bar: 70% uniqueness required
  • Review: Enabled (adds extra chain stage)
  • Web learning: Enabled with caching
  • Logging: Maximum detail (debug + verbose)
  • Naming: climate_dev_001.html, network_dev_002.html, etc.
  • Metadata: Included in all files
  • File limits: 300 KB per file, 10 MB total
  • Speed: Moderate (review stage adds time)

Use Cases

  • Testing new specifications
  • Debugging issues
  • Learning the system
  • Rapid iteration with feedback
  • Quality over speed

Production Profile

File: .claude/config/profiles/production.json

Key Settings

{
  "max_parallel_agents": 5,
  "batch_size": 10,
  "min_uniqueness_threshold": 0.9,
  "enable_review_stage": false,
  "enable_progressive_sophistication": true,
  "output_directory": "output_prod",
  "naming_pattern": "{theme}_{iteration:04d}_{variant}.html",
  "include_metadata": false,
  "web_enhancement.enabled": true,
  "web_enhancement.initial_priming_urls": 5,
  "web_enhancement.cache_web_content": false,
  "logging.level": "warn",
  "logging.verbose": false,
  "limits.max_iterations": 1000,
  "limits.max_file_size_kb": 500,
  "chain_prompting.stages": 7
}

Behavior for 5 Iterations

  • Execution: 1 wave (batch_size 10 > 5 iterations)
  • Parallel agents: 5 (processes all 5 simultaneously)
  • Quality bar: 90% uniqueness required
  • Review: Disabled (faster execution)
  • Web learning: Enabled, no caching (fresh fetches)
  • Logging: Minimal (warn level only)
  • Naming: climate_0001_interactive.html, network_0002_animated.html
  • Metadata: Not included (smaller files)
  • File limits: 500 KB per file, 100 MB total
  • Speed: Fast (no review, parallel processing)

Use Cases

  • Production deployments
  • Large-scale generation (100+ iterations)
  • Performance-critical scenarios
  • High-quality output requirements
  • Minimal overhead needed

Differences from Development

  • 5x faster: 5 agents vs 2, no review stage
  • 1 wave vs 2 waves: Larger batch size
  • Higher quality bar: 90% vs 70% uniqueness
  • No metadata: Cleaner, smaller files
  • Different naming: 4-digit iteration number, variant suffix
  • No caching: Always fresh web content
  • Minimal logging: Less output noise
  • Higher limits: Can handle 1000 iterations vs 10

Research Profile

File: .claude/config/profiles/research.json

Key Settings

{
  "max_parallel_agents": 3,
  "batch_size": 5,
  "min_uniqueness_threshold": 0.95,
  "enable_review_stage": true,
  "enable_progressive_sophistication": true,
  "output_directory": "output_research",
  "naming_pattern": "{theme}_research_{iteration:03d}.html",
  "include_metadata": true,
  "web_enhancement.enabled": true,
  "web_enhancement.initial_priming_urls": 8,
  "web_enhancement.cache_web_content": true,
  "web_enhancement.progressive_difficulty": true,
  "logging.level": "debug",
  "logging.verbose": true,
  "features.enable_cross_iteration_learning": true,
  "limits.max_iterations": 50,
  "limits.max_file_size_kb": 500,
  "chain_prompting.stages": 11
}

Behavior for 5 Iterations

  • Execution: 1 wave (batch_size 5 = 5 iterations)
  • Parallel agents: 3
  • Quality bar: 95% uniqueness required (highest)
  • Review: Enabled (quality feedback)
  • Web learning: Maximum (8 initial URLs, progressive difficulty)
  • Logging: Maximum detail (debug + verbose)
  • Naming: climate_research_001.html, network_research_002.html
  • Metadata: Included with extensive details
  • File limits: 500 KB per file, 50 MB total
  • Speed: Slow (quality-focused, many stages)
  • Extra features: Cross-iteration learning, 11 chain stages

Use Cases

  • Exploring new domains
  • Maximum quality output needed
  • Understanding system capabilities
  • Experimenting with techniques
  • Research and development
  • Publication-quality results

Differences from Development

  • Higher quality bar: 95% vs 70% uniqueness
  • More web priming: 8 URLs vs 2
  • Cross-iteration learning: Enabled (learns from previous outputs)
  • Progressive difficulty: Web sources increase in complexity
  • More chain stages: 11 vs 8 (additional analysis stages)
  • Larger output allowance: 500 KB vs 300 KB files
  • Quality over speed: Sacrifices speed for maximum quality

Side-by-Side Comparison

Feature Development Production Research
Performance
Parallel Agents 2 5 3
Batch Size 3 10 5
Waves for 5 items 2 1 1
Speed Moderate Fast Slow
Quality
Uniqueness Threshold 70% 90% 95%
Review Stage Yes No Yes
Progressive Sophistication No Yes Yes
Cross-Iteration Learning Yes No Yes
Web Enhancement
Enabled Yes Yes Yes
Initial Priming URLs 2 5 8
Cache Content Yes No Yes
Progressive Difficulty No Yes Yes
Output
Directory output_dev output_prod output_research
Naming Pattern {theme}dev{iteration:03d} {theme}{iteration:04d}{variant} {theme}research{iteration:03d}
Include Metadata Yes No Yes
Max File Size 300 KB 500 KB 500 KB
Logging
Level debug warn debug
Verbose Yes No Yes
Log Outputs Yes No Yes
Limits
Max Iterations 10 1000 50
Max Total Output 10 MB 100 MB 50 MB
Warn At 5 100 25
Chain Prompting
Stages 8 7 11
Self-Correction Yes Yes Yes
Features
URL Strategy Yes Yes Yes
Theme Evolution Yes Yes Yes
Cross-Learning Yes No Yes
Auto Indexing Yes Yes Yes

Execution Time Estimates

For 5 Iterations:

Profile Estimated Time Reasoning
Development 3-5 minutes 2 waves, review stage, moderate logging
Production 1-2 minutes 1 wave, 5 agents, no review, minimal logging
Research 8-12 minutes 1 wave, 11 stages, extensive web priming, cross-learning

For 100 Iterations:

Profile Estimated Time Reasoning
Development N/A Max 10 iterations (would need override)
Production 15-25 minutes 10 waves (batch 10), 5 agents, optimized
Research 60-90 minutes 20 waves (batch 5), extensive quality checks

Quality vs Speed Trade-offs

Development Profile

  • Priority: Balance + Learning
  • Quality: Medium (70% uniqueness)
  • Speed: Medium (review enabled)
  • Best for: Testing, debugging, iterating

Production Profile

  • Priority: Speed + Scale
  • Quality: High (90% uniqueness)
  • Speed: High (no review, max agents)
  • Best for: Deployments, large batches

Research Profile

  • Priority: Maximum Quality
  • Quality: Very High (95% uniqueness)
  • Speed: Low (many stages, extensive learning)
  • Best for: Exploration, publication, research

Switching Profiles

How to Switch

Command Syntax:

/project:infinite-config <spec> <output> <count> [profile]

Examples:

# Development (testing)
/project:infinite-config specs/example_spec.md test_out 5 development

# Production (deployment)
/project:infinite-config specs/example_spec.md prod_out 100 production

# Research (exploration)
/project:infinite-config specs/example_spec.md research_out 20 research

No Code Changes Required

Switching profiles changes:

  • ✓ Batch sizes and wave structure
  • ✓ Parallel agent counts
  • ✓ Quality thresholds
  • ✓ Review stages
  • ✓ Web enhancement settings
  • ✓ Logging detail
  • ✓ File naming patterns
  • ✓ Resource limits
  • ✓ Chain prompting stages

All without touching a single line of code.

Custom Configurations

You can also create custom configurations:

# Create custom config
/project:configure create production my_custom.json

# Use custom config
/project:infinite-config specs/example_spec.md output 50 custom my_custom.json

# Override specific values inline
/project:infinite-config specs/example_spec.md output 10 development '{"logging":{"level":"info"}}'

Recommendation Guide

Choose Development When:

  • ✓ Testing new specifications
  • ✓ Debugging generation issues
  • ✓ Learning the system
  • ✓ Need detailed logging
  • ✓ Want review feedback
  • ✓ Small iteration counts (< 10)

Choose Production When:

  • ✓ Deploying to production
  • ✓ Large batch generation (50-1000 iterations)
  • ✓ Speed is critical
  • ✓ Quality requirements known and high (90%)
  • ✓ Minimal logging preferred
  • ✓ System is well-tested

Choose Research When:

  • ✓ Exploring new domains
  • ✓ Maximum quality needed (95%+)
  • ✓ Publication or presentation outputs
  • ✓ Willing to sacrifice speed for quality
  • ✓ Want cross-iteration learning
  • ✓ Need extensive web research

Conclusion

The configuration-driven architecture enables the same codebase to behave completely differently based on the selected profile. This provides:

  1. Flexibility: Switch use cases without code changes
  2. Optimization: Profiles tuned for specific scenarios
  3. Safety: Validation ensures configurations are valid
  4. Reproducibility: Version-controlled configs guarantee consistent results
  5. Scalability: Easy to add new profiles for new use cases

The power of configuration over code.

Document Generated: 2025-10-10 Test Profile Used: Development Files Generated: 5 visualizations + 2 documentation files