Demos are easy. Production is not.
Anyone can wire a language model to a few tools and record an impressive demo. The gap between that demo and a system you can trust with real customer data is where most agent projects die. The difference is rarely the model - it is everything around it.
In production, an agent faces inputs nobody anticipated, tools that time out, and users who phrase the same request a hundred different ways. A production-grade agent is designed for that mess from day one.
Guardrails are an architecture, not a prompt
Telling a model to 'be careful' is not a guardrail. Real guardrails live outside the model: schema validation on every tool call, allow-lists for destructive actions, spending caps, and hard limits on iteration loops.
The pattern we use on client work is simple: the agent proposes, deterministic code disposes. Any action with side effects passes through a validation layer the model cannot talk its way around. High-risk actions queue for human approval.
If you cannot measure it, you cannot ship it
Every agent we ship has an evaluation suite before it has users: a few hundred real-world test cases scored automatically on every change. When the suite is green, we deploy with confidence. When a new failure appears in production, it becomes a test case the same day.
Start smaller than feels impressive. One workflow, fully instrumented and boringly reliable, earns the trust that funds the next five. That is how agents make it past the demo.