What Are AI Agents? | LIZIU AI Agents

What is an AI Agent?

Why AI Agents Matter

The Problem: Traditional chatbots can only respond to messages -- they cannot take action, use tools, or work autonomously toward a goal.

The Solution: AI agents combine LLM reasoning with the ability to perceive their environment, make decisions, and take actions -- creating systems that can accomplish complex tasks independently.

Real Impact: AI agents are powering coding assistants, research tools, customer support systems, and autonomous workflows across every industry.

Real-World Analogy

Think of an AI agent as a skilled employee rather than an answering machine:

Chatbot = A phone answering machine that plays pre-recorded responses
AI Agent = An employee who listens, thinks, uses tools, and completes tasks
LLM = The employee's brain -- capable of reasoning and planning
Tools = The employee's toolkit -- APIs, databases, search engines
Memory = The employee's notebook -- retaining context across interactions

Core Characteristics of AI Agents

Autonomy

Agents operate independently, making decisions without constant human input. They determine the next step based on context.

Tool Use

Agents can call external tools -- APIs, databases, code interpreters -- to interact with the real world beyond text generation.

Reasoning

Goal-Directed

Unlike chatbots that respond turn-by-turn, agents work toward completing a specific objective through multiple steps.

Agent vs Chatbot

Feature	Traditional Chatbot	AI Agent
Interaction	Single turn Q&A	Multi-step task completion
Tool Access	None or hardcoded	Dynamic tool selection and use
Planning	No planning ability	Breaks down goals into sub-tasks
Memory	Conversation history only	Short-term + long-term memory
Error Handling	Returns error message	Retries, adapts, finds alternatives

The Agent Loop

Agent Loop: Perceive - Reason - Act

Core Components of an Agent

simple_agent.py

import openai

class SimpleAgent:
    def __init__(self, system_prompt, tools):
        self.system_prompt = system_prompt
        self.tools = tools
        self.messages = [
            {"role": "system", "content": system_prompt}
        ]

    def run(self, user_input):
        # 1. PERCEIVE: Add user input to context
        self.messages.append({
            "role": "user",
            "content": user_input
        })

        while True:
            # 2. REASON: Call LLM to decide next action
            response = openai.chat.completions.create(
                model="gpt-4o",
                messages=self.messages,
                tools=self.tools
            )

            message = response.choices[0].message

            # 3. ACT: Execute tool or return response
            if message.tool_calls:
                for tool_call in message.tool_calls:
                    result = self.execute_tool(tool_call)
                    self.messages.append(result)
            else:
                return message.content

Types of Agents

Reactive Agents

Respond directly to inputs without internal state. Simple stimulus-response behavior -- fast but limited in complex reasoning.

Deliberative Agents

Maintain an internal model of the world and use planning to decide actions. Better at complex tasks but slower to respond.

Hybrid Agents

Combine reactive and deliberative approaches. Use fast reactive responses for simple tasks and deliberation for complex ones.

Multi-Agent Systems

Multiple specialized agents working together, each handling a different aspect of a complex problem.

Quick Reference

Concept	Description	Example
Agent	Autonomous system that perceives, reasons, and acts	Coding assistant that writes, tests, and debugs code
Tool	External capability the agent can invoke	Web search, database query, code execution
Agent Loop	Continuous cycle of perceive-reason-act	Read error -> analyze cause -> apply fix -> verify
System Prompt	Instructions defining agent behavior and capabilities	"You are a research assistant with access to..."
Memory	Retained context across interactions	Conversation history, user preferences, past results