AI agents have spent the last year proving they can impress in a demo. The harder, more valuable question is whether they can hold up in production, where they face messy data, real users, and the quiet expectation that they simply work.
Start with a job, not a model
The teams that succeed with agents do not start from the model. They start from a clearly defined job to be done, then work backwards to the smallest agent that can do it reliably. That discipline keeps scope tight and outcomes measurable.
We treat every agent like a junior team member: give it a narrow remit, clear instructions, and the tools it needs, then review its work until we trust it. Only then do we widen its responsibilities.
Make it observable
An agent you cannot watch is an agent you cannot trust. We instrument every step so we can see what the agent decided, why, and what it did next. When something goes wrong, the trail tells us exactly where to look.
Design for graceful failure
Production is unpredictable, so we plan for the moments an agent gets stuck. Clear fallbacks, human handoffs, and sensible defaults turn a potential failure into a small, recoverable hiccup rather than an outage.
