AI Agents: the next stop for LLM apps — how do they actually land in the enterprise?

2025 was declared the year of the AI Agent. From OpenAI's Operator to Tongyi, Wenxin and Doubao, every major vendor shipped an agent product. Behind the noise, one question matters: how do agents actually land in enterprise scenarios?

Three challenges to enterprise adoption

First, reliability — enterprise tolerance for error is near zero while hallucination isn't solved. Second, security — enterprise data can't simply be uploaded to third-party model services. Third, cost — agent inference costs far exceed plain API calls.

The right way to open enterprise agents

The pragmatic path: private deployment plus vertical fine-tuning. Build local knowledge bases, combine them with RAG (retrieval-augmented generation) so agents operate within controllable knowledge. Multi-agent collaboration is the next technical direction.