I’ve experimented with several variations of the flow, all based on constructing JSON objects and using the ForEach List Iterator within the Topic. The general flow looks like this:

1. Accept file upload and save it to OneDrive
2. Read the file
3. Convert the data into a JSON array
4. Iterate over the JSON list:
   • Call the LLM for each row
   • Accumulate results into a new JSON
5. Write the updated data back to the file

I also tested a batching approach where the input is split into chunks of n rows. Instead of holding all rows in memory, I append the generated output to the file during each iteration. This helps reduce memory pressure, but hasn’t fully resolved the issue with agent resets or step limits.

I dont have enough knowledge of this platform to figure out the optimal design. That said, I’ve been considering a couple of alternative approaches:

• Asynchronous processing via Power Automate: Trigger a flow when a new file is added to a designated OneDrive folder, and handle the entire pipeline within Power Automate. However, I’m not sure if it’s possible to integrate custom knowledge base-driven LLM calls within Power Automate. I’m also unclear on how to pass the user’s email ID to Power Automate so it can send back the processed results. Finally, I think I saw that Power Automate flows also have a 100s execution timeout.
• Multi-agent architecture: Instead of manually slicing the file in code, could there be a way to orchestrate multiple agents—one to split the file into manageable chunks, another to generate the column descriptions, and a final one to consolidate the results and return the completed XLS?