In this era, foundation models are highly touted, but what transforms a base model into a product is software engineering.

In the age of AI agents, the software engineering system of the East has an underestimated engineering organizational capability: scaffolding, standardization, interface design, rapid delivery, outsourced process management, and engineering template reuse.

The foundation model itself is not a product. A model is merely a probabilistic engine. To truly accomplish tasks, it needs an outer layer: task decomposition; tool invocation; browser control; file system; code execution; permission systems, and so on.

These things may sound less sexy than the foundation model, but they determine whether an agent is just a demo or a real product.

Good scaffolding can make the foundation model twice as effective with half the effort.

In the agent era, many capability differences don't come from the model itself, but from the external execution system.

The model is the engine, and the scaffolding is the transmission, chassis, brakes, dashboard, and road rules. A powerful engine without a chassis and control system cannot produce a stable product.

The software industry in the East has long been under several pressures:

First, requirements change rapidly.

Client demands often change, and product managers frequently make revisions.

Second, there is a strong delivery orientation.

Teams in the East strongly emphasize one thing: get the system delivered first. This culture has accumulated a lot of technical debt in traditional software, but it has an advantage in the early stages of agent products because what agents lack most is the delivery capability from demo to workflow.

Third, deep experience with outsourcing and project-based systems.

Breaking down modules, writing interfaces, establishing standards, integrating systems, and delivering on time. This set of capabilities is very suitable for building the engineering shell of an AI agent.

Fourth, extensive training with super apps and complex business systems.

Engineering teams in the East have long worked on payments, logistics, e-commerce, customer service, review, recommendation, CRM, mini-programs, low-code platforms, and the enterprise WeChat ecosystem.

They naturally focus on permissions, processes, approvals, state machines, and exception handling. These are exactly what agents need to integrate into enterprise workflows.

The ability to integrate unstable intelligence into stable business processes is not something that can be measured by the capabilities of the foundation model itself.