With the rapid development of the AI field, the ways AI is used in practical business are changing, and you will find that its role is evolving.

In fact, AI has gradually entered the execution phase, such as triggering trading instructions, participating in operational process scheduling, influencing resource allocation order, and even directly impacting real profits in some scenarios. This change is largely a natural extension to higher-responsibility business layers as model capabilities mature.
Parallel to these trends is the lag in underlying system architecture. Many AI systems are still designed around single requests and responses, lacking management of long-term states and systematic records of continuous actions.
When AI behaviors begin to span across time, participate in multi-stage processes, and have cumulative effects on outcomes, the structure centered on “single output” gradually reveals its limitations.
As execution enters the real business chain, challenges start to focus on the infrastructure layer. Whether execution actions are traceable, verifiable, and can be incorporated into responsibility and settlement systems is becoming a prerequisite for the system’s long-term reliability.
Long-term behaviors need to be continuously recorded, collaborative relationships need to be clearly decomposed, and results need to be understandable and reviewable.
These conditions may not be determined solely by the model’s capabilities but depend on whether the underlying system has a structural design capable of supporting execution behaviors.
From resource networks to execution experience: Melos’s practical starting point
Looking back at Melos’s development over the past few years, it did not originate from the concept of intelligent agents. Early Melos was closer to