I still remember the first AI system I deployed to production. It was 2019, and I was convinced it would change everything. Within 48 hours, it had crashed three times, hallucinated customer data, and sent an automated email to our CEO calling him "Dear Valued Spam."
That failure taught me more about AI engineering than any course ever could.
The Gap Nobody Talks About
There's a chasm between AI that works in a Jupyter notebook and AI that works in production. It's not a technical gap—it's a philosophical one. In research, you optimize for accuracy. In production, you optimize for reliability, observability, and graceful degradation.
The metrics that matter change entirely:
- Latency matters more than benchmark scores
- Failure modes matter more than success rates
- Explainability matters more than complexity
The Three Pillars of Production AI
After shipping dozens of AI systems, I've distilled what matters into three principles:
1. Design for Failure
Every AI system will fail. The question is: how gracefully? I now build every system with explicit fallback paths. If the model fails, what happens? If the API times out? If the input is malformed?
2. Observe Everything
You can't fix what you can't see. Every production AI system needs comprehensive logging, tracing, and alerting. Not just for errors—for behavior. What inputs cause unexpected outputs? Where does confidence drop?
3. Iterate Ruthlessly
The first version will be wrong. Ship it anyway. Learn from real usage. Improve. The teams that iterate fastest win.
What I'm Building Now
These lessons led me to create tyingshoelaces—an open-source platform for building production-ready AI agent systems. It's opinionated. It's battle-tested. And it embodies everything I wish I'd known in 2019.
If this resonated, follow me for more stories from the trenches of AI engineering.