10383b40b7
5 contracts, 16 positions, 10K worker pool. Four agents: Matcher (SQL
+ vector hybrid), Communicator (LLM SMS drafts), Verifier (fact-checks
against golden data), Analyzer (RAG intelligence questions).
Results:
- SQL matching: 16/16 positions filled, ZERO hallucinations. Every
worker's name, role, city, state, certifications, and reliability
score verified against the golden dataset.
- SMS generation: 16/16 messages drafted with correct worker names.
- RAG intelligence: retrieval returns semantically similar but
structurally wrong workers (wrong state, wrong archetype) because
vector search can't do structured filtering. LLM correctly reports
context limitations — doesn't hallucinate beyond retrieved chunks.
Key finding: SQL path is production-ready. RAG path needs hybrid
SQL+vector routing — SQL for structured constraints (state, role,
cert, reliability), vector for semantic similarity. That's the
architectural gap to close.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>