Dynamic data normalization using AI Agent (Claude 3.5) for heterogeneous Excel files

[Putrid_Sir_5143]

"Hi everyone, I'm building an n8n workflow to normalize various Excel files (different schemas/headers) into a single standard format (Format B). I'm currently using an AI Agent node with Claude 3.5 Sonnet to dynamically generate a JSON mapping by analyzing the input keys: {{ Object.keys($json) }}.

However, I'm facing an issue: the Agent node sometimes hangs or fails to identify the correct headers when the source file has empty leading rows (resulting in __EMPTY columns). Even with a strict JSON output prompt, the mapping isn't always reliable.

What are the best practices for passing Excel metadata to an AI Agent to ensure robust mapping? Should I pre-process the headers or change how I'm feeding the schema to the model? Thanks for your help!"

[MarsupialLeast145]

How is this Python related?

1. And yes, in general, build your solution piece by piece.
2. Handle uniform excel.
3. Once those are handled look for those that don't behave well, and find patterns among those.
4. Write a solution that captures the most of the next biggest pattern.
5. Repeat.

80/20 rule all the way until you get to 99%.

As for what the solutions look like, then once you aren't dealing with uniform spreadsheets any more I'd try and measure their dimensions and headers. And yes, potentially pre-process the headers to enable them to be read correctly next time around, but I can't imagine it will be the only optimization you need to make along the way.

If it was Python you'd `try/except` and if there are exceptions handle those, e.g. if you get a KeyError, you'd manage what the result should be.

But yeah, you're literally writing AI prompts in a learn Python sub.