The Gap Between AI Demos and Production
What happens when AI agents meet reality—incomplete data, adversarial conditions, users who don't read instructions.
TL;DR: AI demos work because they’re designed to work. Production systems face incomplete data, hostile inputs, confused users, and a thousand edge cases nobody anticipated. This is where the interesting engineering problems actually live.
Why Demos Work
What Breaks in Production
- Incomplete data: users omit context, fields, or intent.
- Hostile inputs: prompt injection, boundary pushing, “weird” user behavior.
- Real-world usage: users don’t read instructions and don’t follow workflows.
- Brittle dependencies: APIs time out, tools fail, and latency changes outcomes.
How You Close the Gap
- Defensive input handling: validate, normalize, and default early.
- Output verification: check constraints, schemas, and confidence signals.
- Feedback loops: capture failures, label them, and fix the system—not the prompt.
- Operational mindset: measure, monitor, and plan for the “1% cases” that dominate support.