What is an AI agent and how does it differ from a standard LLM?

Beginner · 2 min read · AI Agents & MCP

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Short answer: A standard LLM is a stateless text-in, text-out model that generates completion tokens based on a static prompt; an AI agent is an autonomous system that uses an LLM as its central reasoning engine to execute multi-step plans, invoke external tools, maintain state, and evaluate environment feedback to achieve a goal.

Detail

The transition from a raw LLM to an AI Agent introduces three key operational loops:

                  ┌───────────────────────────┐
                  ▼                           │
[User Goal] ──► [LLM Planner] ──► [Tool Call] ──► [Environment Observation]

Perception & Planning: The LLM receives goals, formulates plans, and selects actions.
Action & Tool Execution: Executing API calls, database queries, shell scripts, or web browsing.
Observation & Reflection: Feeding execution outputs and errors back into the LLM context to determine next steps or retry failed operations.

Core Dimension	Standard LLM	AI Agent
Execution Style	Single forward-pass generation	Multi-turn reasoning loop
Tool Integration	None (pure text generation)	External APIs, DBs, search engines, sandboxes
State & Memory	Ephemeral context window	Persistent memory (short-term & long-term)
Autonomy	Passive responder	Active goal-directed executor

Example

Conceptual Python structure of an agent execution loop:

python

class Agent:
    def __init__(self, llm, tools: dict):
        self.llm = llm
        self.tools = tools
        self.memory = []

    def run(self, goal: str, max_steps: int = 5):
        self.memory.append({"role": "user", "content": goal})

        for step in range(max_steps):
            response = self.llm.generate(self.memory)
            if response.is_final_answer:
                return response.answer

            # Execute tool action selected by LLM
            tool_result = self.tools[response.tool_name](**response.tool_args)
            self.memory.append({"role": "tool", "content": str(tool_result)})

        return "Max steps reached without completion."

Interview tips

Emphasize that agent loops require strict termination criteria (max step counts, timeout bounds, and budget limits) to avoid infinite execution loops.
Highlight the role of structured function calling (JSON schema) in driving reliable agent tool invocation.

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