What Exactly Is an AI Agent? Complete Interview Guide

Interview Question Overview

The author introduces the interview question "What is an Agent?" and explains why AI agents have become a must‑talk topic in both AI‑focused and general software development interviews.

Key Assessment Points

Understanding the distinction and boundaries between LLMs and Agents

Familiarity with the definition and core components of an Agent

Awareness of real‑world challenges when deploying Agents

Standard Answer

Agent is an LLM‑centered computational entity that possesses planning, memory, and tool‑use capabilities, enabling it to autonomously decompose complex tasks, iterate, perceive feedback, and close the task loop, moving from passive text generation to autonomous task execution.

Extended Follow‑up Questions

Difference between an Agent and a plain LLM chat interface – Agents can set goals, break down tasks, invoke tools, and loop until completion, while LLMs only generate a single response.

Handling infinite loops – Set a maximum iteration count, add a reflection mechanism, or involve human intervention.

Suitable business scenarios – Non‑fixed workflows, multi‑step cross‑tool tasks, and tasks that require continuous optimization based on feedback.

Typical Use Cases

Code generation, execution and debugging (e.g., Cursor, Claude Code)

Automated data‑analysis report generation

Customer‑service agents that process orders, diagnose issues, trigger refunds and send confirmation emails

Detailed Component Analysis

1. LLM – the “CPU” of the Agent

The LLM replaces traditional if‑else rules and handles intent understanding, task planning, tool selection, and result evaluation.

2. Planning (ReAct paradigm)

ReAct combines reasoning and acting; the main workflow is shown in the diagram below. Limitations include dependence on LLM reasoning ability, rapid context‑window growth in long tasks, and possible dead‑loops.

3. Memory

Short‑term memory uses the LLM’s context window (e.g., 128 KB, 256 KB, 1 MB) and can lose early information as the conversation grows. Long‑term memory relies on Retrieval‑Augmented Generation (RAG) with vector databases to store important facts and retrieve them when needed.

4. Tool Calling (Action)

LLMs can emit structured JSON function calls; external code executes the actual tool. Best practices include minimizing permissions, sandbox isolation, timeout and fallback handling, and output validation.

5. Multi‑Agent Systems

Multiple agents can cooperate by dividing responsibilities, enabling more complex business processes.

Pseudo‑code Example

The following Python‑style pseudo‑code demonstrates an Agent that reads a CSV sales file, generates an analysis report, and iterates until a final answer is produced.

def agent_executor(user_goal):
    # 1. Initialize memory and system prompt
    memory = []
    system_prompt = "You are a data‑analysis expert capable of file I/O, charting, etc."
    while True:
        # 2. Send goal, memory, and tool descriptions to LLM
        response = llm.predict(
            prompt=system_prompt + user_goal + str(memory),
            tools=available_tools
        )
        # 3. Parse LLM output
        if response.type == "final_answer":
            return response.content
        elif response.type == "tool_call":
            tool_name = response.tool_name
            args = response.args
            # 4. Execute tool and get observation
            observation = execute_tool(tool_name, args)
            # 5. Store thought‑action‑observation in short‑term memory
            memory.append(f"Thought: {response.thought}")
            memory.append(f"Action: {tool_name}({args})")
            memory.append(f"Observation: {observation}")