# Recipes

- [Percolating Python-first](https://percolation-labs.gitbook.io/percolation-labs/recipes/percolating-python-first.md): Most of the agentic frameworks in use today are Python-based. Many of them rely heavily on Pydantic. In this section we take a look at how Percolate and Python work together.
- [Percolating SQL-first](https://percolation-labs.gitbook.io/percolation-labs/recipes/percolating-sql-first.md): The real power of Percolate is its native SQL interface and rich integration with data and data governance. In this section we dive into the SQL functions that can be used to build agentic AI.
- [Percolate for SREs](https://percolation-labs.gitbook.io/percolation-labs/recipes/percolate-for-sres.md)
- [No-code Percolate](https://percolation-labs.gitbook.io/percolation-labs/recipes/no-code-percolate.md)
- [Founder's DataRoom P8](https://percolation-labs.gitbook.io/percolation-labs/recipes/founders-dataroom-p8.md)
- [Document Drafter P8](https://percolation-labs.gitbook.io/percolation-labs/recipes/document-drafter-p8.md)


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://percolation-labs.gitbook.io/percolation-labs/recipes.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.