> ## Documentation Index
> Fetch the complete documentation index at: https://blog.elevate.do/llms.txt
> Use this file to discover all available pages before exploring further.

# Rube Goldberg AI Workflows

> Musings + AI Generated Art, by Rahul Parundekar. Published on 12th May, 2025.

<img height="200" src="https://mintcdn.com/elevatehumanexperiencesllc/xQbLc86ONDS7PMgy/images/ChatGPT%20Image%20May%2012,%202025,%2010_40_28%20AM.png?fit=max&auto=format&n=xQbLc86ONDS7PMgy&q=85&s=d0edc3dc08f860e13f8460d4c86b37bd" title="Rube Goldberg." data-path="images/ChatGPT Image May 12, 2025, 10_40_28 AM.png" />

> Image generated using GPT 4o on May 12th, 2025.

You folks know what a Rube Goldberg machine is, right?

As I implement at-scale complex multi-step workflows for my clients, I'm reminded of how the non-deterministic nature of LLMs compounds with multiple downstream steps. Combine that with standard engineering realities—rate limits, flaky connections, retries, timeouts—and it quickly becomes more than a toy problem. It becomes real engineering.

Take one of my current workflows: it takes **15 minutes** to complete. That’s not idle time—it’s churning through PDF parsing, database reconciliation, structured-to-unstructured transformations, all of which carry their own failure probabilities.

At one point, I parallelized tasks using `ThreadPoolExecutor`, but rolled it back after too many hard-to-debug errors. Turns out, a pesky 503 from Bedrock was derailing things mid-run.

At this scale, you’re not just prototyping—you’re building distributed, fault-tolerant systems. AI workflows need the same care we give to production infra.

So, yeah… the comic above? It’s a little funny, but also a little too real.

All this… from one reply. Welcome to the future of autonomous growth.

***

🛠️ Curious how to make your AI agents production-ready? Drop me a note.

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