ShippingAIagentssafelyinproduction

The gap between an impressive agent demo and a production system doing real work is enormous — and most teams underestimate it by an order of magnitude. Over the last 18 months we’ve deployed autonomous agents into ops, support and engineering pipelines across a dozen clients. Here’s what actually held up in production.

Start with an evaluation set, not a model

Before you choose GPT-5 or Claude 4 or a fine-tune, invest a week building a golden eval set of 200–500 realistic tasks with graded outputs. Everything else — model choice, prompt strategy, tools — becomes a search over this set instead of an argument in Slack.

Guardrails are a system, not a prompt

The most reliable agents we’ve shipped combine a strong system prompt with hard structural guardrails: schema-validated tool inputs, output linters, allowlisted action registries, and a supervisor loop that reviews any decision above a risk threshold.

A kill switch you’ve rehearsed

You will have to turn your agent off. Design the switch, the observability that will trigger it, and the recovery playbook — and rehearse it — before you launch. This is the difference between a scary Monday morning and a boring Monday morning.