claude/update-claude-md-tKX5F

[drzo]

🚀 Pull Request: AI Inference Engine Compliance

Summary

✅ AI Engine Implementation Checklist

Please confirm the following:

• All AI agent logic uses real inference (e.g., llama.cpp, node-llama-cpp, BERT models)
• No mock/stub/placeholder logic remains in AI execution paths
• llama.cpp or equivalent is integrated and invoked in all relevant modules
• Inference output has been tested with real prompts
• Fallbacks (if any) still use a valid quantized model, not a mock
• All changes follow the SKZ_INTEGRATION_STRATEGY.md production phase requirements
• Environment variables are configured for AI model paths and settings
• AI model loading and caching is implemented properly

🔍 Validation Steps

Describe how you validated the AI engine functionality:

# Example validation commands
cd skz-integration/autonomous-agents-framework
source venv/bin/activate

# Test AI inference functionality
python -c "from src.models.ml_decision_engine import DecisionEngine; engine = DecisionEngine(); print('AI engine loaded successfully')"

# Verify no mock usage in production
python validate_production_quality.py

# Test inference endpoints
curl http://localhost:5000/api/v1/agents/test-inference

🧠 AI Model Configuration

Describe the AI models and inference engines used:

• Primary Engine: llama.cpp / node-llama-cpp / BERT model
• Model Path: /path/to/model/files
• Quantization: q4_0 / q8_0 / float16
• Context Length: 2048 / 4096 / custom
• Fallback Model: Smaller quantized variant

📎 Related Issues / Strategy Milestone

Link to any related issues or production phase tasks:

• Closes #XXX (Replace AI mock with llama.cpp)
• Related to SKZ_INTEGRATION_STRATEGY.md#phase-3-production-execution-engine

🧪 Testing Requirements

• AI inference tests pass with real models
• Performance benchmarks within acceptable limits
• Memory usage optimized for production deployment
• Error handling validates graceful degradation
• Integration tests verify end-to-end AI workflows

🛡️ Production Quality Verification

• No TODO, mock, or placeholder in AI agent code
• All AI functions return real inference results
• Production configuration validates successfully
• CI/CD pipeline AI validation passes
• Load testing confirms inference scalability

🧠 Notes for Reviewers

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CRITICAL: This PR must comply with zero-tolerance policy for mock AI implementations. All inference must use real models.