What We Learned Shipping AI Agents Into Production

A demo that works once is easy. An agent your team trusts to run unattended, every day, without surprising anyone — that's a different engineering problem entirely.

The Demo-to-Production Gap

Demos are forgiving: you pick the happy path, you're watching closely, and a weird output is a curiosity, not an incident. Production has none of that grace. The same agent needs guardrails, logging, and a fallback for every step that can fail.

What Actually Closes the Gap

• Constrain the agent's action space — fewer tools, tighter scopes
• Log every decision, not just the final output
• Build a human-review path for low-confidence actions
• Treat prompt changes like code changes: reviewed and versioned

Reliability came less from a smarter model and more from boring, disciplined engineering around it.