We upgraded our AI agent from string matching to actual understanding

Short summary

The author upgraded their AI agent (OUROBOROS) by replacing ten shallow keyword-matching primitives with semantic embeddings using all-MiniLM-L6-v2. Task similarity detection improved from 0.0 (Jaccard) to 0.7+ on paraphrased tasks like 'optimize queries' vs 'speed up SQL'. A unified embedding module with graceful degradation reduces maintenance complexity while enabling efficient caching.

• •Replaced 10 shallow keyword-matching primitives with semantic embeddings (all-MiniLM-L6-v2)
• •Similarity detection improved from 0.0 to 0.7+ on paraphrased task descriptions
• •Unified embedding module with graceful fallback reduces maintenance and enables 60-70% cache hit rates

Generated with AI, which can make mistakes.