In the generative AI landscape, an AI Agent is distinct from a basic chatbot or standard foundation model because of its ability to act autonomously to execute multi-step objectives.
The correct definition is an application that learns how to achieve a goal based on inputs and tools available to it (C). An agent is built around a core Large Language Model (LLM) which serves as its " brain. " Given an objective or goal from a user, the agent uses a reasoning loop (such as ReAct) to evaluate inputs, break the goal into sub-tasks, and call external tools (like APIs, web search, databases, or calculators) to interact with the external world and accomplish the task.
Option A is incorrect because agents are dynamic and action-oriented, not static.
Option B describes a " Human-in-the-loop " supervisor, not the AI agent itself.
Option D describes a chat interface or frontend wrapper, which is merely a way to communicate with an agent, not the definition of the agent ' s functional architecture.
(Reference: Google Cloud ' s official architecture guides for Generative AI define an Agent as an autonomous software entity driven by an LLM that accepts natural language goals, breaks them down into executable workflows, and leverages tools to alter states or retrieve information to satisfy that goal.)
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