What is an AI Agent?

What is an AI Agent?

An AI agent is a system that perceives its environment through sensors, reasons about what it perceives, and takes actions through actuators to achieve specific goals.

The Agent-Environment Interaction Loop

Every AI agent follows a fundamental cycle:

1. Perceive — The agent observes its environment through sensors (cameras, microphones, text input, API data, etc.)
2. Reason — The agent processes observations and decides what to do (using rules, ML models, or LLMs)
3. Act — The agent takes an action that affects the environment (sending a message, moving, calling an API)
4. Feedback — The environment changes, and the cycle repeats

Agents vs. Traditional Programs

Aspect	Traditional Program	AI Agent
Input	Fixed, predefined	Dynamic, from environment
Decision-making	Hardcoded logic	Adaptive reasoning
Autonomy	None	Partial to full
Learning	No	Often yes
Environment awareness	No	Yes

A Simple Example

Consider a thermostat:

• Sensor: Temperature sensor
• Reasoning: If temperature < target, turn on heater
• Actuator: Heater switch
• Environment: The room

This is the simplest form of an AI agent — a simple reflex agent.

Modern AI Agents

Today's AI agents are far more sophisticated. An LLM-powered agent like ChatGPT:

• Perceives through text input (and images, files)
• Reasons using a large language model
• Acts by generating text, calling tools, writing code
• Adapts through conversation context

# Conceptual structure of an AI agent
class SimpleAgent:
    def __init__(self, tools, llm):
        self.tools = tools
        self.llm = llm
        self.memory = []

    def perceive(self, input):
        self.memory.append({"role": "user", "content": input})

    def reason(self):
        response = self.llm.generate(self.memory)
        return response

    def act(self, decision):
        if decision.requires_tool:
            result = self.tools[decision.tool_name].execute(decision.args)
            return result
        return decision.text

Key Takeaways

• An AI agent is defined by its ability to perceive, reason, and act autonomously
• Agents exist on a spectrum from simple (thermostat) to complex (autonomous vehicles)
• Modern agents often use LLMs as their reasoning engine
• The agent-environment loop is the fundamental pattern underlying all agent architectures