Single agent multi agent vs hierarchical agents

AI agents can be categorized based on their operational frameworks into single-agent, multi-agent, and hierarchical systems, each serving distinct purposes and complexities. Single-agent systems focus on scenarios where one agent operates independently to solve problems, making decisions based solely on its own perceptions and objectives. In contrast, multi-agent systems involve multiple AI agents interacting or competing within a shared environment, necessitating coordination and communication strategies to optimize collective outcomes. Hierarchical agents utilize a structured approach where decision-making processes are layered, allowing for sophisticated control mechanisms where higher-level agents govern the actions of lower-level agents, enabling complex, scalable solutions.
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