Shipping AI agents that survive contact with production

Most AI agent pilots die in the same place: somewhere between a convincing demo and a production rollout, when real customer input starts arriving in shapes nobody scripted for.

The gap isn't model quality. It's that a demo only needs to work once, in front of an audience that wants it to succeed. Production needs to work the ten-thousandth time, against input from someone actively trying to break it or just having a bad day.

Evaluation is infrastructure, not an afterthought

Before we ship an agent, we build the evaluation harness first — a set of real, adversarial, and edge-case inputs the agent is scored against on every change. Without that, 'the agent seems better' is a feeling, not a fact you can ship on.

• Define what 'correct' means for the task before writing the agent
• Log every input and output for review, not just the failures you catch
• Set explicit escalation paths for low-confidence or high-stakes decisions
• Track cost and latency as product requirements, not afterthoughts

The teams that get real value from agents treat them like any other production system: versioned, tested, monitored, and owned by someone who gets paged when it misbehaves.