Hybrid AI: Combining Deterministic Analytics with LLM Reasoning

LLMs consistently fail at reliable numerical analytics — even with Code Interpreter enabled, they skip rows, apply wrong filters, and fabricate plausible-looking results. A hybrid architecture solves this by strictly separating concerns: an Analysis Planner LLM translates natural language requests into deterministic JSON Selection Rules, while an Analysis Engine executes a fixed pandas Python script against the actual data. The LLM never touches raw data directly. Only after deterministic execution produces validated results does the parent LLM agent interpret and communicate findings to the user. This approach is demonstrated end-to-end with a manufacturing assessment use case involving 800+ column Excel files.