However. Instead of asking an LLM to "do the whole thing" (which is indeed prone to inconsistency) and thus letting LLMs run wild like unsupervised toddlers maintaining the "do not press" buttons at a nuclear facility. A FLAT approach puts control and predictability to LLM interactions by treating them more like traditional programming constructs but enhanced with LLM's natural language understanding. like:

- Binary decisions (gates) - Limited classification (using match/case) - Structured data extraction (using Pydantic models) - well typed function calling

Anywho, Would love to hear your thoughts on an experiment F.L.A.T (Frameworkless LLM Agent... Thing) https://github.com/mindsdb/flat-ai

Showcasing that it is possible to leverage the power of LLMs in Agents though absolute simplicity: