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Multi-agent Systems
Definition
Multi-agent systems are systems composed of multiple AI agents that interact, coordinate, or collaborate to achieve shared or individual goals within a common environment.
Purpose
The purpose of multi-agent systems is to decompose complex problems into smaller, specialized tasks that can be handled by separate agents, improving scalability, robustness, and flexibility compared to single-agent approaches.
Key Characteristics
- Presence of multiple autonomous or semi-autonomous agents
- Coordination or communication mechanisms between agents
- Task decomposition and role specialization across agents
- Potential for parallel execution and distributed decision-making
- Emergent system behavior resulting from agent interactions
Usage in Practice
In practice, multi-agent systems are used to solve complex, distributed, or dynamic problems where coordination between multiple agents is required, such as workflow automation, simulation, monitoring, and large-scale decision support systems.
One implementation of this concept is offered by Kenaz through the AI Agents Pipeline service.