Why ToB AI Agents Fail: Model Limits and the Tech‑Business Gap
The article analyzes why ToB AI agents struggle to succeed, pinpointing two core issues: inadequate model capabilities that force temporary engineering patches, and a disconnect between technical staff who understand AI and business staff who understand domain needs.
Implementing ToB (business‑to‑business) AI agents faces two fundamental obstacles.
Model capability is insufficient
When the underlying model cannot meet required performance, teams resort to ad‑hoc engineering fixes. These temporary patches keep the project afloat only while engineering support exists; once that support disappears, the solution is likely to collapse.
The gap between technology and business
Technical personnel know how to use AI and build agents, but often lack insight into actual business processes. Conversely, business experts understand their domain but are unaware of what AI can deliver. Without close collaboration, the AI’s potential cannot be realized.
An exception occurs when the same group handles both roles, such as in AI‑assisted coding where developers build and use the system themselves, achieving high efficiency—though this also accelerates the risk of the technology rendering the developers obsolete.
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