AI Agent: Definition and Key Components

What is an AI agent? There is no single definition, but leading companies agree on the general characteristics. Anthropic describes an agent as a system in which an LLM dynamically manages processes and tools. Hugging Face defines it as AI that interacts with its environment to achieve a goal. Sber adds three criteria: planning, executing a plan, and autonomy.

A simple example: a user asks for the dollar exchange rate. The agent forwards the request to the LLM, which has tools at its disposal: a calculator, web search, and code execution. The LLM chooses the search option, retrieves the result, and formulates a response. This demonstrates autonomy—the selection of a tool without human intervention.

Key Components of an AI Agent

LLM

This is the agent’s “brain.” An LLM predicts the next word based on the input text. It’s important to consider the language of the data (Russian-language models, such as Vikhr, or multilingual ones) and the type of task (different models excel in different areas).

Tools

External functions that an LLM can call: APIs, databases, web search. Tools extend the agent’s capabilities beyond text generation.

Prompts

System instructions that define the agent’s behavior. A well-written prompt ensures predictable performance.

Memory

There are two types: short-term (conversation context) and long-term (databases, vector stores). Memory allows the agent to “remember” past interactions.

Planning and Reasoning

The agent must break down complex tasks into steps. Techniques such as Chain-of-Thought or ReAct are used for logical inference.

Conclusion

An AI agent is a system that combines an LLM, tools, prompts, memory, and planning. Understanding these components is the first step toward creating your own agent.

Source: Habr