What are AI agents, and how do they differ from standard large language model deployments?

AI agents are large language models (LLMs) enhanced with the capability to use tools or execute functions, allowing them to interact with and modify external environments in autonomous or semi-autonomous ways, often with human-in-the-loop oversight [1][2][4][9]. This enables agents to perform actions like accessing software, conducting web searches, making API calls, or managing calendars by generating and running commands [3]. They promise to automate computer-based tasks across various domains, though their real-world use and reliability remain under scrutiny [1][9][12]. In contrast, standard large language model deployments typically function as chatbots or text generators without integrated tool execution, focusing on passive reasoning or content creation rather than active environmental interaction [10]. While sophisticated LLM users may delegate complex tasks to models as cognitive tools [7], agents extend this by structuring outputs for deterministic tool invocation, addressing non-determinism in LLMs to ensure safe and reliable actions [9]. Multi-agent systems further evolve this by enabling collaborative workflows beyond single-model limitations [6][10][11].