Multi-Agent Systems

Also known as: MAS, Multi-Agent, AI Orchestration, Agent Network

Architectures where multiple specialized AI agents collaborate to complete complex tasks by dividing and coordinating work.

Multi-Agent Systems (MAS) are architectures where multiple AI instances—each specialized in a specific task—collaborate, communicate, and coordinate to complete complex objectives that would exceed the capabilities of a single agent.

In a research workflow, there could be a questionnaire designer agent, a data analyst agent, a visualization generator agent, and a report writer agent, all orchestrated by a coordinator agent. This division of labor increases speed, specialization, and output quality.

Popular multi-agent frameworks in 2026: AutoGen (Microsoft), CrewAI, LangGraph, and orchestration systems from Anthropic and OpenAI. Market research is one of the use cases with the greatest potential for these systems given its nature as a sequential workflow with differentiated steps.

The primary risk is error propagation between agents: if one agent makes a mistake, subsequent ones may amplify it. Validation at each stage is critical.