[Feat] Schema-aware enhancement for SQL Agent generation

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Hi @LinuxSuRen,

I'm currently exploring the `AI-Powered API Testing Agent` projects in OSPP, and I find the idea of using an AI agent to convert natural language into SQL really awesome!!! I believe it could greatly help non-technical users interact with data more naturally. (As a student, I often feel quite overwhelmed when working with complex or large datasets, and it can be really challenging...)

While reading through the project goals and thinking from a user perspective, I wondered:

Would it make sense to enrich the agent's SQL generation with some form of schema-awareness?

For example, if a user types:
"Show me all disabled accounts"

But the database schema has a field like `status = 'deactivated'` or `is_active = false`, a naive prompt-to-SQL conversion might not catch that. In this case, a retrieval step — even something lightweight like schema inspection or doc-based enrichment — might help improve both accuracy and robustness.

I'm still trying to understand the current architecture better, so this is more of a question than a suggestion. But I'd love to know:

Has this direction been considered before?Would you be open to exploring something like this in the future?

If this is a valuable direction, I'd be very interested in trying to contribute something along these lines after understanding the system more clearly.

Thanks again for your awesome work on this project! I’m really enjoying digging into it.

The issue is a question about future schema-aware SQL generation in the AI-Powered API Testing Agent, but it names no files, tests, or entry points. Start by mapping the agent architecture and current natural-language-to-SQL flow. Done would require an agreed design and demonstrated improvement in SQL generation, which this issue does not yet define.

Written by the indexing model from the issue text.