Multi-agent system to extract,route,summarize data

A multi-agent system (MAS) utilizes specialized autonomous AI agents that collaborate to extract raw information, selectively route it to the appropriate processing pipelines, and summarize it into actionable insights. This decentralized workflow prevents context bottlenecks and allows for scalable, highly parallel data handling.The architecture operates via an interconnected lifecycle to manage raw data:1. Extraction Phase: The pipeline ingests raw, unstructured or semi-structured information from documents, emails, or databases. Specialized Extraction Agents use parsing utilities to clean text, strip out irrelevant noise, and convert the raw input into standardized JSON or structured formats. Routing Phase: Instead of using basic keyword matching, the system employs a Router Agent to analyze. Summarization Phase: Once routed, domain-specific Summarizer Agents condense the targeted information. By keeping individual agents focused on narrow mandates, the system prevents irrelevant data from bloating the memory and ensures the final summary remains highly accurate and concise.