Why AI Engineering Is Becoming More Like Distributed Systems Engineering

As foundation models continue to improve, I think AI engineering is starting to look far more like distributed systems engineering.

The difficult part usually is not the model itself - it is everything around it:

• Orchestration
• Retries
• Queues
• Workflow state
• Observability
• Evaluation
• Scaling

A production AI workflow can very quickly become:

1. Retrieval
2. Multiple LLM/tool calls
3. Async processing
4. Validation
5. Downstream systems

At that point, you are dealing with classic system problems rather than just prompting.