Multi-Agent System

Multi-Agent System is a software architecture where multiple, specialized AI agents interact and collaborate to solve complex problems. By dividing a task among agents with defined roles, the system achieves higher accuracy and handles larger workflows.

How it Works

Multi-agent systems distribute work across a network of agents coordinated by an orchestrator or communication protocol.

• Role Specialization: Assigns narrow capabilities to each agent (e.g., one agent writes SQL, another analyzes data results, and a third generates reports).
• Inter-Agent Communication: Defines standard protocols for agents to exchange data, request assistance, or review each other’s outputs.
• Coordination Layer: Employs routing patterns (such as supervisor trees or state machine loops) to control task progression and resolve agent conflicts.

Lakehouse & Agentic Relevance

In a data lakehouse, analytical workflows involve multiple stages, from data ingestion to BI visualization. In a multi-agent system, a query agent can generate the SQL, while a validation agent reviews the query against catalog schemas, and a reporting agent parses the result set. Dremio facilitates this cooperative workflow by serving as the unified data access point. Each specialized agent can query Dremio’s virtualized datasets to perform its task, ensuring a single source of truth.