What are the three core components of a Hugging Face Agent architecture?

• A. A dataset, a trainer, and a deployment script.
• B. A user interface, a database, and a cache.
• C. A planner, a critic, and a scorer.
• D. An LLM (reasoning core), a set of Tools (capabilities to invoke), and an Agent loop/Executor that plans and acts.

Answer: (D)

Understanding how a Hugging Face Agent is built: at its heart is an LLM that serves as the reasoning core, a set of Tools that provide the capabilities the agent can call (like searching the web, running code, or accessing data), and an Agent loop/Executor that plans what to do, decides which Tool to use, carries out the action, and then updates its plan based on the result. This trio creates a loop where the reasoning engine proposes actions, the tools perform them, and the loop feedback tightens the agent’s behavior over time. The other options describe different parts of ML pipelines or system components (datasets and training, or interfaces and storage, or high-level policy components) that aren’t the specific architectural building blocks of a Hugging Face Agent.