Table of Contents
What Defines Multi-Agent Systems vs Single-Agent in AI Workflows?
Learn how multi-agent systems operate via information sharing and task delegation among specialized agents—core to CrewAI certification, enabling scalable, collaborative AI for complex automation and deployment.
Question
How do multi-agent systems work?
A. A multi-agent system involves the sharing of information or delegation of tasks among agents.
B. A multi-agent system has one agent handle multiple tasks in a batch job.
C. A multi-agent system runs one agent multiple times, then selects the best result from among those outputs.
D. A multi-agent system has one agent switch between multiple roles during runtime.
Answer
A. A multi-agent system involves the sharing of information or delegation of tasks among agents.
Explanation
Multi-agent systems function through collaborative interactions where multiple autonomous AI agents share information, delegate subtasks, and coordinate efforts to achieve complex objectives that exceed single-agent capabilities, often via communication protocols, hierarchical structures, or peer negotiation.
This delegation enables specialization—such as one agent handling research while another performs analysis—and emergent behaviors like load balancing or conflict resolution, contrasting with single-agent limitations in scalability and robustness. In frameworks like CrewAI, agents exchange context through shared memory or messages, dynamically routing tasks based on capabilities, which supports real-world applications from logistics to software orchestration without centralized bottlenecks.