This research introduces a multi-agent architecture capable of autonomously discovering insights from real-time data streams, shifting the paradigm from passive to active analytics.
Background
In streaming data environments, traditional analytics systems often struggle because users must manually define queries over massive and constantly changing datasets. The new system leverages Apache Kafka for event orchestration and Apache Flink for stream processing, integrated with LLMs to power specialized agents.
Why It Matters
The key to this architecture is a contract-driven design based on strongly-typed intermediate artifacts. This enhances observability, data lineage, and safety when executing dynamically generated analyses. For financial and retail enterprises in Vietnam, this solution paves the way for building 'opportunity self-discovery' systems rather than relying passively on static reports.