TECHNOLOGY
works, not at the surface level but at the level of why a particular step in a workflow exists, what happens when it goes wrong and what good looks like. That takes years to build. You can’ t acquire it quickly.
And the place that really shows up is in the edge cases. Edge cases are almost always what cause these technologies to fail. The clean, happy path is easy to automate. It’ s the strange exception, the account that doesn’ t reconcile, the file stuck in dispute, the situation nobody mapped, that breaks things. Human beings are good at exactly this. They make judgment calls on the edge cases. And if you’ re not operating the process every day, you simply don’ t know what those cases are. You can consult on how a process should look and configure it in a system, but until you’ re running it day in and day out, you never learn where it actually breaks. We know those cases because we do this work across a huge variety of use cases for hundreds of companies.
Technical expertise is the AI and engineering capability to build and deploy systems that actually work in production, not in a demo environment. And it’ s not just building it once. The question is how you build a foundation that drives value in weeks or months, not years and then compound off that foundation rather than starting from scratch every time.
What that means in practice is building agentic workflows that power reimagined business processes, not just faster versions of the old ones. Those workflows depend on data that’ s properly organised, accessible and delivering the right context to both humans and agents at the right moment. The agent engineering has to be built to scale, and equally important, built to adapt as models evolve, because they will keep evolving. That means staying disciplined about how you consume tokens, which tools and skills you deploy and how the agent is moving the process forward rather than just responding to it.
And then there’ s the layer most people don’ t plan for: the infrastructure and support required over the lifetime of the system. These aren’ t deploy-and-forget implementations. They require ongoing
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