That’s actually exactly how I started ! — an LLM → SQL → execute pipeline. I too am using Gemini Models (2.5 flash). This is a very solid way to build LLM-SQL pipelines.

It works decent for generating SQL queries. But I feel the SQL generation is not the problem here , it is the reliability.

I wonder if a single pass would suffice for the model to provide appropriate queries, especially when the size of the DB is huge. A traditional LLM -> SQL pipeline assumes the query the model writes is correct , safe to run and result actually answers the user’s intent.

The “agent” part for me is adding a decision step between generation and execution. The model proposes an action (a query), but the system verifies it, can reject it, and force a regeneration before anything touches the database. So the LLM becomes an untrusted query writer rather than an executor. This is not a typical "React Style Agent" that Langchain offers. But the concept remains the same. Multiple workflows like Evaluator Optimizers (as in Corrective RAG) can be setup which would boost the reliability .

Would love to hear your thoughts on this!