Need 1 real workflow payload where bad AI output would actually cause damage

Questions
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1

I’m testing a narrow boundary for AI workflow execution.

Not a chatbot.
Not a broad AI platform.
Just one question:

when should a workflow continue, and when should it stop safely?

I’m looking for 1 real n8n-style workflow case where bad AI output is actually costly downstream.

Examples:

  • document / invoice extraction

  • ticket routing

  • compliance / category classification

  • anything where wrong structured output causes manual cleanup, bad routing, or broken downstream steps

What I need:

  • 1 sample payload

  • 1 target schema

  • 1 short note on what goes wrong downstream if the output is wrong

  • polling or webhook preference

What I return:

  • either succeeded or failed_safe

  • short failure classification if relevant

  • a public-safe receipt / trust artifact

This is intentionally narrow.
I’m not trying to onboard teams into a big product.
I just want one real case to test whether this boundary is useful in practice.

If you have one, reply here or DM me.

Public kit:
https://github.com/kodomonocch1/dlx-public-kit

2

Hi @kodomonocch1
Interesting… i think invoice extraction is a point where a lot of serious errors can happen with ERP injection, somewhat you can consider this as an incoming payload from a webhook trigger:

{
  "file_url": "https://example.com/invoice_4821.pdf",
  "vendor_hint": "ABC Supplies",
  "received_at": "2026-04-01T10:15:00Z"
}

and in the AI agent turn the output parser on and we want AI to output like this:

{
  "invoice_number": "string",
  "vendor_name": "string",
  "invoice_date": "YYYY-MM-DD",
  "due_date": "YYYY-MM-DD",
  "currency": "ISO_4217",
  "total_amount": "number",
  "tax_amount": "number",
  "line_items": [
    {
      "description": "string",
      "quantity": "number",
      "unit_price": "number",
      "line_total": "number"
    }
  ]
}

Now you can see there are a lot of things which could go wrong if the AI model is not powerful enough, like wrong total amount or maybe currency, and more like invoice number, vendor name etc.. so the error range only first happens in the AI getting the input, also the AI does not read things like we do, same with how we view things.

So for this kind of flow if we are talking to create something like that i would create it in such a way so that the incoming invoice gets validated by at least 2 different AI models(Strong once) and there in those checks once we are confirm with the information, i will pass the data to the main AI agent, and there the BEST POSSIBLE approach is to involve a human in the loop, in case if we dont i will setup a different flow which will validate the incoming data from the AI agent that is it right or not and there i will give certain error parameters like schema_invalid totals_mismatch low_confidence_vendor missing_required_field

There can be a lot of errors specifically related to AI agent but a lot of those can be solved just by using the BEST model.

3

Thanks — this is useful, and invoice extraction into ERP is exactly the kind of downstream risk I care about.

The payload shape, schema, and risk notes are helpful.

For Phase 1, I’m not trying to optimize around “best model” selection yet.
What I’m testing first is whether a guarded boundary should end in succeeded or failed_safe before downstream continuation.

So the most useful next step would be:

  • this same case as a real anonymized workflow example
  • the target schema
  • a short downstream risk note
  • return method: polling

If you can share it in that form, I’ll treat it as a priority test case.

4

@kodomonocch1 consider this publicly available template as your start, then modify it on your needs.

You should focus on the LLM Model you are gonna use, the AI agent is only as good as the model you use, so for now focus on the LLM inference provider you need and then try using the models to configure the kind of output you expect:

5

Thanks — useful references.

For Phase 1, I’m not looking for a public template or a model/provider comparison.
What I need first is one real anonymized workflow case to test the boundary against an actual downstream risk.

If you have one, the most useful next step is:

  • 1 sample payload
  • 1 target schema
  • 1 short downstream risk note
  • return method: polling

If not, no worries — I’ll keep this case focused on real workflow examples first.

1 Like
6

@kodomonocch1 sounds like a good job posting.

1 Like
7

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