Agent
IntermediateA system that perceives state, selects actions, and pursues goals—often combining LLM reasoning with tools and memory.
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Why It Matters
Agents are fundamental to the development of autonomous systems and intelligent applications. By understanding how agents operate, researchers and developers can create more sophisticated AI technologies that can adapt to dynamic environments and perform complex tasks effectively.
An agent in the context of artificial intelligence is a system that perceives its environment, selects actions, and pursues goals based on its internal state and external inputs. This concept can be mathematically represented through Markov Decision Processes (MDPs), where the agent's decision-making is modeled as a sequence of states, actions, and rewards. Agents often integrate various components, including planning algorithms, reinforcement learning techniques, and memory systems, to enhance their autonomy and effectiveness. The study of agents is closely related to fields such as multi-agent systems and autonomous robotics, where the focus is on the interaction and cooperation between multiple agents in complex environments.