Until recently, the modernisation of customs processes was a race to digitise paperwork and automate manual workflows. With that phase largely complete, we are entering a far more consequential era: the shift from automated systems to agentic ones. We are moving toward systems that not only follow rules, but also largely configure themselves, adapt to regulatory shifts in real-time, and interact with human experts through natural language.
From software engineering to policy engineering
The fundamental bottleneck in traditional customs systems is the “translation gap.” When a tariff schedule is amended or a new risk indicator is introduced, software engineers must manually translate legal text into system code. This process is slow, expensive, and creates a dangerous lag between policy intent and operational reality.
Large Language Models (LLMs) are closing this gap. Instead of a six-month development cycle, an analyst can now describe a change in natural language. The system interprets the instruction, drafts the logic, and once verified by a human expert, applies it to the operational environment almost instantaneously. This reduces reliance on rigid software cycles and places the power directly back into the hands of the policy specialists who understand trade best.
