Recently, a knowledge graph-driven communication expert intelligent agent independently developed by China Mobile Research Institute has been successfully launched at China Mobile Jiangsu Company. This intelligent agent is built on a communication knowledge graph foundation and equipped with a "pattern recognition + large model" hybrid Q&A framework, effectively addressing bottlenecks in wireless network intelligent transformation including high professional knowledge barriers, difficulty in reusing expert experience, and cross-domain data silos. It establishes a new foundation for wireless network cognitive decision-making and opens new pathways for large-scale application of intelligent agents in live networks.

Knowledge graphs, based on graph data models, store multi-source heterogeneous data in structured form, significantly enhancing the retrieval and reasoning capabilities of complex information and providing crucial support for professional domain research. China Mobile Research Institute has integrated the "knowledge graph + large model" collaborative framework to independently develop a knowledge graph-driven communication expert intelligent agent. On one hand, by constructing a domain knowledge graph covering 25 categories of communication knowledge including network architecture, signaling processes, and parameter configurations with over 100,000 nodes, it breaks down cross-domain data silos. On the other hand, relying on the hybrid framework to balance rule certainty with AI flexibility, it builds a cognitive decision-making foundation for wireless networks.

Compared to general large models that require over 500B computing power but still suffer from missing professional knowledge graphs and shallow professional responses, this intelligent agent uses only a 7B parameter Q&A model. Under edge computing constraints, it achieves a hallucination rate below industry average and high accuracy of 88.9%.

At the graph construction level, the communication expert intelligent agent relies on entity alignment technology to support cross-language adaptation and dynamic incremental updates, achieving logical association of domain knowledge. At the model optimization level, it innovatively proposes a hybrid Q&A framework collaboration mechanism: the pattern recognition layer achieves millisecond-level responses to high-frequency questions through rule engines; the large model layer analyzes complex semantic queries based on a 7B parameter model; the hybrid decision mechanism improves response accuracy through dual-engine collaboration.

The core innovation of this technology lies in deeply integrating the semantic understanding capabilities of large models with the structured constraints of knowledge graphs. On one hand, it uses dynamic knowledge sources to compensate for large model deficiencies in professional domains; on the other hand, it utilizes logical associations between knowledge graph entities to assist reasoning, effectively reducing large model hallucinations.

The knowledge graph-based communication expert intelligent agent has broad application prospects in live networks. In general technical Q&A, the intelligent agent integrates high-quality domain knowledge to provide traceable signaling process analysis, message parameter queries, and other highly reliable answers, effectively suppressing model hallucinations. In troubleshooting assistance, based on live network operation and maintenance case knowledge graphs, it generates precise investigation recommendations and solutions more aligned with actual scenarios. In business experience optimization, through correlation analysis of terminal measurement status and resource scheduling potential root causes, it provides specific performance optimization recommendations.

With knowledge graph support, this intelligent agent achieves performance comparable to general full-scale large models using only a 7B parameter model. In scenarios such as parameter fine-tuning configuration, its accuracy even surpasses billion-level general models, creating possibilities for lightweight deployment of edge-side intelligent agents.

As AI RAN technology penetrates all network elements, China Mobile Research Institute reshapes wireless intelligent agent architecture with "knowledge graph + hybrid AI," providing live networks with intelligent agent decision engines that "understand, inquire thoroughly, and solve quickly." The research team will continue advancing technology integration, connecting knowledge graphs with existing multi-dimensional intelligent perception evaluation and guided fault diagnosis systems to form a "perception - analysis - decision" closed loop, accelerating technology achievement transformation.

Through deep integration of communication technology and artificial intelligence, this intelligent agent will possess cognitive reasoning capabilities equivalent to communication experts. With knowledge graphs as the core foundation, it will provide a reusable technical base for large-scale replication and continuous evolution of network intelligence, injecting solid momentum into comprehensive intelligent upgrades of communication networks.