Agentic Intelligence for Scalable Metadata Mining in Telecom Big Data Systems

The high rate at which telecommunication services are changing has yielded a volume of heterogeneous data never seen before such as call detail records, signaling data, and subscriber activity traces. These datasets are crucial in relation to optimization and business intelligence, but effective use of such datasets requires quality metadata. Traditional metadata management is mostly manual and centralized, which cannot support the pace and scale of the new telecom big data systems. As a solution to these shortcomings, the present article suggests a scalable metadata mining strategy that is founded on agentic intelligence. In comparison to more traditional machine learning pipelines, agentic intelligence enables the use of autonomous goal-oriented AI agents, capable of reasoning in a hard data space and relevant to new network topologies and network schemas. The proposed architecture will include a multiagent architecture at a distributed level, having specialized agents: (1) schema drifting ingestion and monitoring agents of data flows; (2) semantic mining actors based on natural language processing to infer contextual metadata; (3) process-aware agents for process mining to determine lineage and dependencies between data; and (4) compliance with policy and metadata retrospect: government agents. A major effect of the work is the integration of agentic intelligence and scalable infrastructures of big data such as cloud-native analytics platforms and distributed data lakes. These agents coordinate themselves using lightweight coordination mechanisms to provide a persistent metadata knowledge layer that facilitates system interoperability and robust downstream analytics. The design concepts are geared toward horizontal scalability and fault tolerance, enabling the distribution of the agents at dynamically evolving manner regarding the workload peak. Besides, agents also enhance their strategies upon continuous feedback to handle concept drift as well. Typical examples of telecom applications such as monitoring network performance and fraud detection have demonstrated that such an approach far outperforms a rule-based approach in the accuracy, coverage, and timeliness of metadata. And lastly, the framework will be capable of offering more dynamic and sustainable data management within the telecom sector.