tractatus/docs/UPDATE_PLAN.md

Documentation & Stress Testing Plan

Date: November 3, 2025 Purpose: Update all references to Agent Lightning + CPU stress testing

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Part 1: Documentation Updates

A. Website Pages to Update

1. Homepage (public/index.html)

Current status: Says "Now integrating with Agent Lightning" Update needed: "Agent Lightning integration operational (CPU training)"

Locations:

• Hero subtitle
• "What's New" section
• Community section

Action: Update wording from "integrating" to "operational"

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2. Persona Pages

public/researcher.html

Check: What does it say about AL? Update: Reflect operational status + research opportunities

public/implementer.html

Check: Implementation guides accurate? Update: Add real integration examples

public/leader.html

Check: Business case still accurate? Update: Real metrics from stress testing

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3. Integration Page (public/integrations/agent-lightning.html)

Status: ✅ Already updated today Content: Accurate operational status

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B. Documentation Files

1. GitHub README (docs/github/AGENT_LIGHTNING_README.md)

Status: Pushed to GitHub Check: Still accurate after today's changes? Update: May need operational status update

2. Integration Guides

• docs/integrations/agent-lightning.md
• docs/integrations/agent-lightning-guide.md

Update: Add real implementation examples, stress test results

3. Demo Documentation

• demos/agent-lightning-integration/README.md
• Demo 1 & 2 READMEs

Update: Clarify conceptual vs real integration

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C. Translation Files

Check if translations need updates for:

• "integrating" → "operational"
• New status messaging

Files:

• public/locales/en/common.json
• public/locales/de/common.json
• public/locales/fr/common.json

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Part 2: CPU Stress Testing

A. Test Suite Design

Test 1: Analyzer Performance Benchmark

Purpose: Measure analysis speed, accuracy, consistency

Metrics:

• Time per analysis (ms)
• Throughput (analyses/second)
• Memory usage (MB)
• CPU utilization (%)

Dataset: 100 synthetic feedback examples (varied types)

Expected:

• <5 seconds per analysis (acceptable)
• <1 second per analysis (good)
• <500ms per analysis (excellent)

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Test 2: Reward Function Consistency

Purpose: Verify rewards are stable across runs

Test:

• Run same feedback through analyzer 10 times
• Measure reward variance
• Check category consistency

Expected:

• Same feedback → same category (100% consistency)
• Reward variance <0.1 (stable scoring)

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Test 3: Concurrent Load Testing

Purpose: Test multiple feedback submissions simultaneously

Test:

• 10 concurrent analyses
• 50 concurrent analyses
• 100 concurrent analyses

Metrics:

• Response time degradation
• Error rate
• Memory pressure
• CPU saturation point

Expected:

• 10 concurrent: <10% slowdown
• 50 concurrent: <50% slowdown
• 100 concurrent: Identify CPU limit

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Test 4: Error Handling

Purpose: Verify graceful degradation

Tests:

• Invalid feedback (empty comment)
• Extremely long feedback (10,000 chars)
• Malformed data
• LLM timeout/failure

Expected:

• No crashes
• Appropriate error messages
• Reward penalties (-0.5) for failures

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Test 5: Category Accuracy (Manual Validation)

Purpose: Validate analyzer categorizations

Process:

1. Run analyzer on 50 diverse examples
2. Manually review each categorization
3. Calculate accuracy rate
4. Identify problem patterns

Expected:

• 80% accuracy (acceptable)

• 90% accuracy (good)

• 95% accuracy (excellent)

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Test 6: MongoDB Query Performance

Purpose: Test feedback data pipeline

Tests:

• Load 1000 feedback entries
• Query by type/rating/page
• Aggregate statistics
• Concurrent reads

Metrics:

• Query time (ms)
• Index effectiveness
• Connection pooling

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B. Baseline Metrics to Collect

Performance Metrics:

• Analysis time (mean, p50, p95, p99)
• Throughput (analyses/second)
• Memory usage (idle, peak)
• CPU utilization (mean, peak)

Quality Metrics:

• Category accuracy (%)
• Severity accuracy (%)
• Reward consistency (variance)
• False positive rate (%)

System Metrics:

• MongoDB query time (ms)
• Network latency (ms)
• Error rate (%)
• Uptime (%)

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C. Stress Test Implementation

File: al-integration/testing/stress_test.py

Features:

• Automated test suite
• Metrics collection
• Report generation
• Baseline documentation

Output:

• STRESS_TEST_REPORT.md
• Metrics JSON for tracking
• Performance graphs (optional)

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D. Comparison: CPU vs GPU (Future)

CPU Baseline (Today):

• Analysis time: X ms
• Throughput: Y analyses/sec
• Memory: Z MB

GPU Target (MS-S1 Max):

• Analysis time: X/10 ms (10x faster) [NEEDS VERIFICATION]
• Throughput: Y*10 analyses/sec [NEEDS VERIFICATION]
• Memory: Z MB + GPU VRAM

This validates "5% performance cost" claims with REAL DATA

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Part 3: Update Deployment Strategy

Phase 1: Audit (30 minutes)

1. Check all pages for AL mentions
2. Document current wording
3. Identify what needs changing

Phase 2: Updates (1-2 hours)

1. Update homepage (hero, what's new)
2. Update persona pages (researcher, leader, implementer)
3. Update documentation files
4. Update translations if needed

Phase 3: Stress Testing (2-3 hours)

1. Build stress test suite
2. Run all tests
3. Collect baseline metrics
4. Document results

Phase 4: Documentation (1 hour)

1. Create STRESS_TEST_REPORT.md
2. Update integration docs with real metrics
3. Update website with performance data

Phase 5: Deployment (30 minutes)

1. Deploy all website updates
2. Commit stress test code
3. Push documentation updates

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Part 4: Expected Outcomes

Documentation Updates:

✅ All pages reflect "operational" status ✅ No false claims remain ✅ Real implementation examples ✅ Accurate technical details

Stress Testing:

✅ CPU baseline metrics documented ✅ Performance bottlenecks identified ✅ Error handling validated ✅ Category accuracy measured ✅ Real data for claims validation

Benefits:

✅ Confidence in CPU deployment ✅ Baseline for GPU comparison ✅ Data-driven optimization ✅ Honest performance claims ✅ Research integrity maintained

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Priority Order

High Priority (Do first):

1. Stress test suite (proves it works)
2. Collect baseline metrics (proves performance)
3. Homepage update (most visible)
4. Integration docs update (technical accuracy)

Medium Priority: 5. Persona pages update 6. Translation files 7. GitHub README review

Low Priority (Can wait): 8. Demo documentation polish 9. Planning documents archive

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Success Criteria

Documentation:

• All pages say "operational" not "in development"
• Real metrics cited (from stress tests)
• No false claims
• Translations updated

Stress Testing:

• All 6 test categories passed
• Baseline metrics documented
• Performance report published
• Bottlenecks identified

Deployment:

• Website live with updates
• Docs committed to git
• Stress test code in repo
• Metrics tracked over time

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Timeline

Session 1 (Today):

• Build stress test suite
• Run initial tests
• Document baseline metrics

Session 2 (Tomorrow):

• Update all pages
• Deploy to production
• Commit documentation

Total: 4-6 hours work

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Notes

Why Stress Testing Matters:

• Validates "REAL implementation" claims
• Provides data for "5% cost" comparison
• Identifies CPU limitations before GPU
• Baseline for optimization
• Research integrity (cite real numbers)

Why Documentation Updates Matter:

• Removes last false claims
• Shows progress to community
• Demonstrates research integrity
• Attracts collaborators with honest status

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Status: Ready to execute Owner: Claude Code Review: User approval before deployment