Rube Goldberg AI Workflows

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.

​

Get Connected, Share, and Other Socials