From capability to dependability
The public conversation about AI fixates on capability — what a model can do at its best. In production, the binding constraint is almost always dependability: what a system does reliably, at its worst, on its hundred-thousandth call of the day.
Closing that gap is an engineering problem, not a research one. It lives in evaluation harnesses, fallbacks, guardrails, observability and the unglamorous discipline of treating model behaviour as something to be measured rather than admired.
Agents that plan, act and self-correct
Automated agents are most useful where the workflow is too complex to script in advance. That same property makes them hard to trust: the system is deciding, not following a fixed path.
We design agents around a tight loop — plan, act, observe, correct — and around the assumption that any individual step can be wrong. Robustness comes from the loop, not from any one decision being perfect.
Engineered for production, not demos
A model that performs brilliantly in a notebook and unpredictably under load has not been deployed; it has been previewed. The work of deployment is everything that happens after the impressive result.
That is where we spend our effort: latency budgets, graceful degradation, cost control and the operational tooling that lets a team run an intelligent system the way they would run any other critical service.