What is a Multi-agent AI?
FAQs
A single agent operates independently, solving tasks using its own capabilities without interacting with others. A multi-agent system involves multiple agents working together or competing, sharing information, and coordinating to solve complex tasks beyond the capacity of a single agent.
- Simple reflex agents: React based on current environment state.
- Model-based reflex agents: Use internal models to predict future outcomes.
- Goal-based agents: Focus on achieving specific goals.
- Utility-based agents: Prioritize actions for maximum performance.
- Learning agents: Adapt and improve through experience.
To use multiple AI agents, define roles, enable communication, and design a shared environment for interaction. Implement coordination strategies like task allocation and ensure human oversight to manage conflicts. Test and optimize the system for scalability and efficiency.
Applications range from robotics and logistics to computer games and academic research. They’re especially valuable for tackling distributed and complex tasks.
Through well-defined communication protocols and decision-making mechanisms, agents can coordinate effectively to achieve collective goals.