Using AI Assistants to Actually Get No-Code Workflows Working

Table of Contents

1. Giving AI assistants direct access to tool UIs is still cursed

You’d think with how far large language models have come, you could just say “connect Airtable to Pipedrive and update the lead status” and your assistant would get it done in like, 30 seconds. What happens instead is it opens a million tabs, gets stuck authenticating with OAuth via a popup window that it can’t click, and quietly dies in a background Chrome profile you forgot you were still running.

This is exactly what happened when I tried using the AI assistant inside Bardeen to push an update from a Google Sheet row into an Intercom contact. The assistant understood the goal. It even generated a beautiful little flow of steps in its UI. But once it had to actually perform something like ‘Click the “Edit” button’ inside the Intercom interface … nothing. The iframe context was different from the main tab, so DOM targeting failed completely. It just stopped, no message.

I spent an hour assuming it had succeeded. Only noticed it didn’t when I logged into Intercom and saw zero changes.

There are a few environments (mostly internal, like Notion or Slack) where embedded AI tools work well. Anything that relies on cross-tab navigation or external API auth? 90% chance the LLM gets stuck in visual automation hell.

The workaround I’ve started depending on: using assistants to generate the automation logic (e.g., a properly structured Make scenario or Zapier JSON), but paste/export this into a non-AI builder manually. It’s dumb that the assistant knows what to do — and can’t actually do it without breaking.

2. AI prompt chains break when the previous step has formatting

This edge case caught me three different times before I realized it wasn’t me — it was how the data was being piped from prompt to prompt. I was chaining OpenAI prompts in Zapier: Step 1 reformats a row’s description field into JSON, Step 2 summarizes that JSON to extract intent, Step 3 turns the intent into a support triage response. It worked beautifully on test runs.

Then, live data started causing blank outputs in Step 3. No errors, just empty fields. Turned out in Step 2, the formatting was coming through with trailing spaces and Markdown-style nesting from the previous step. It wasn’t invalid JSON — it just wasn’t clean, and GPT didn’t know what to do.

Fix was stupid simple: insert one step before prompt 3 that just ran a Formatter->Text->Clean Whitespace step. Added a regex clean. Everything started working again. Wild that ChatGPT can simulate a courtroom, but chokes on two spaces in a row.

"summary":   "Request to cancel subscription. User unhappy."
…that’s all it took to break it. Three spaces after the colon.

Also doesn’t help that Zapier AI sometimes applies quote styles to outputs depending on how long the field is. That’s not documented anywhere — I only caught it by inspecting the raw payload on a webhook test.

3. Relying on AI summaries of webhook payloads will bait you

One weird thing that happened last month: I plugged a Discord-to-Gmail notification via Make and added a GPT step to ‘summarize the payload with key context’. I figured it would pull off the usual magic — grab the sender, the subject, and a quick label of priority. And it did. Until it didn’t.

One Saturday, a teammate messaged our bot with three URLs and a short note about a broken flow. The AI summary said: “User requesting help, non-urgent.” But two of the links were to suspended Zapier tasks and Make scenario errors. The AI saw the polite wording (“hey can someone look?”) and missed the urgency.

A better prompt helped, but not entirely

I tried rewriting the prompt to add instructions — like “assume broken links are urgent” — and used an if-then block to escalate if any of the URLs contained “error” or “429”. The fix helped maybe 80% of the time. But without manually parsing webhook payload structure — which changed depending on the Discord role of the sender — it was always shaky. Simpler just to split it:

Step 1: AI labels urgency
Step 2: Regex scans for known red flags
Step 3: Discord role check via lookup table

Only after all three passed did I let the summary through. Annoying, but otherwise I’d keep getting “Summary: chill note” messages five hours after a production task had been failing silently.

4. Natural language variable mapping fails in Airtable automation AI

I almost bought the notion that Airtable’s AI could write automations for non-builders — drag-n-drop a few fields, tell it what you want, and done. The moment you try to reference lookup fields or linked records though? The assistant does some complete nonsense.

I asked it to “when status is changed to Done, send an email with task title and assignee” — seemed simple. Email triggered as expected. Except in the email body, the task title came through as “[object Object]” and the assignee field showed “undefined”.

Turns out both fields were lookups, and Airtable AI can’t distinguish between the visual label and the underlying record structure. In the background, it was piping in the full record object from the linked field — but formatting it as a raw object string.

The fix (which took me two hours to figure out) was creating formula fields to extract the text from the lookups, and reference those instead. Which then broke in another automation because formula fields don’t update instantly when multiple fields change in sequence.

I get why they’re abstracting this — making it “easy” to build workflows — but the moment you try to use AI assistance on anything except flat, single-table data, it just gives up in a really confident voice.

5. ChatGPT Custom GPT memory breaks in shared workspaces

I built a custom GPT assistant to help our project managers draft client onboarding emails. Trained it with a few style guides, gave it labeled email examples, even set up memory to remember preferences like writing tone or greeting style.

Worked brilliantly … until I shared it with one team member who used it in a logged-in ChatGPT Pro session. The assistant suddenly started defaulting all salutation fields to “Hi there” — even though we’d specified in memory to use first names.

There’s no visible setting for this, but turns out each user’s interaction loads a separate memory chain, even on the same shared GPT. And if any custom instruction is updated once, it resets the whole chain for that user. My teammate had tweaked the input prompt one time to change the reply format — and from that moment on, their memory forked from mine.

Plan B was to disable memory entirely and pipe all context each time via the prompt… which actually worked more predictably. I now just pass a variable block with all preferences up front. It’s clunky, but at least it doesn’t silently fork and forget its own rules.


[Client Name]: John 
[Greeting Style]: Use "Hey [first name]"
[Tone]: Warm + slightly casual

…dump it every prompt. Not elegant. Just works.

6. Trigger behavior drifts when building GPTs with file upload inputs

This is a weird one and I have no idea if it’s a bug or just undocumented unexpected behavior. I built a vertical assistant via OpenAI’s GPT builder to process PDFs — mostly invoices and marketing receipts. I added sample files during training and wrote examples for how to respond depending on document type. It worked great in testing. Uploaded five files, parsed consistently.

Then, two weeks later, I noticed totally different behavior. What used to generate a full itemized summary was now just replying with vague notes like “This appears to be an invoice. Please confirm.”

The pattern: any time the uploaded PDF had annotations — like highlight markups, Note comments, or redactions — the model hesitated. Even though the original samples also had those, somehow the AI started treating them as corruption signals. Might have to do with the PDF parser being updated or something under the hood at OpenAI. But it wasn’t mentioned in their changelog.

I tried retraining with new annotated files to reinforce the pattern. Didn’t help. Only fix was stripping annotations from inputs before upload. I now have a Make scenario that auto-flattens PDFs in CloudConvert before sending them to the GPT.

Bonus discovery: if the file is over ~2.5MB, GPT silently fails to process in the first 30 seconds — user sees no output unless they prompt again. Could recreate it multiple times. Nothing logged.

7. Parsing assistant outputs only works well when you give examples

Okay this one’s technically my fault, but it led to an unexpected pattern I haven’t seen documented. I built a quick assistant to generate SOP drafts based on bullet-point inputs from team chat. The AI’s job: convert scattered notes into checklist format with section headers.

Prompt was something like: “Summarize the following as a checklist. Use succinct headers and bulleted items.” And yeah — it DID output checklists. But the section label styles shifted with every output. One was bolded. Next run had underlines. Then we got some italics and once — I kid you not — emojis for header bullets.

None of which would’ve mattered except I was piping this into Notion via a Make scenario, rendering Markdown. But some of the styles weren’t valid Notion Markdown (they use a subset). So, parts of the content failed to render, or worse — skipped silently.

Once I added an explicit output example inside the prompt — actual headers + bullets + spacing — the style finally stuck. Even then, GPT forgot sometimes and reverted after five or six runs. Started wrapping it in a code block to force formatting predictability:


## Setup Checklist
- Confirm build environment
- Verify integrations
- Assign QA lead

…Like THAT, every time.

I now just always prompt GPT with formatting blocks I want back. Never assume it’ll repeat its own layout choices. It won’t.