Hans Wang

The issue is not that they are "not intelligent enough." The real problem is that their engineering paradigms make it difficult to satisfy several core requirements of enterprise-grade customer service at the same time: non-linear conversations, smooth interaction, fast response times, strict rule enforcement, traceability across long conversations, and debuggability for complex business logic.

Let's start with ReAct Agents.

A typical ReAct Agent allows the large language model to autonomously decide, through multi-step reasoning, what to do next, which tools to call, and how to move the task forward. This looks flexible, but in enterprise customer service scenarios, it quickly exposes several problems:

- It behaves more like a black box, making its actions difficult to fully predict;

The issue is not that they are "not intelligent enough." The real problem is that their engineering paradigms make it difficult to satisfy several core requirements of enterprise-grade customer service at the same time: non-linear conversations, smooth interaction, fast response times, strict rule enforcement, traceability across long conversations, and debuggability for complex business logic.

Let's start with ReAct Agents.

A typical ReAct Agent allows the large language model to autonomously decide, through multi-step reasoning, what to do next, which tools to call, and how to move the task forward. This looks flexible, but in enterprise customer service scenarios, it quickly exposes several problems:

- It behaves more like a black box, making its actions difficult to fully predict;