Technical Definition

An AI agent is an autonomous software system that uses Large Language Models (LLMs) as its reasoning engine to dynamically control application flow, make decisions, and execute actions to achieve specific goals. Unlike traditional software with predetermined logic paths, agents can adapt their behavior based on environmental conditions, available tools, and learned experiences.

Autonomy

Operates independently without step-by-step human guidance, initiating actions and modifying approaches based on feedback

Reasoning

Uses LLMs to process natural language, analyze scenarios, and formulate multi-step plans to achieve objectives

Tool Usage

Dynamically selects and utilizes external tools, APIs, and services to extend capabilities beyond base training

Memory & Learning

Maintains state across interactions and improves performance through various feedback mechanisms and experience

Traditional Approach - Fixed Logic

def process_customer_inquiry(inquiry):
    if "refund" in inquiry.lower():
        return "Please contact our refund department"
    elif "shipping" in inquiry.lower():
        return "Shipping takes 3-5 business days"
    else:
        return "Please contact customer service"

# Fixed, predetermined responses
# No learning or adaptation
# Limited to predefined scenarios

AI Agent Approach - Dynamic Reasoning

class CustomerServiceAgent:
    def __init__(self, llm, tools):
        self.llm = llm
        self.tools = {
            'order_lookup': OrderLookupTool(),
            'refund_processor': RefundProcessorTool(),
            'knowledge_base': KnowledgeBaseTool()
        }
        self.memory = ConversationMemory()
    
    def process_inquiry(self, inquiry, customer_id):
        # Agent reasons about inquiry and selects tools
        context = self.memory.get_context(customer_id)
        plan = self.llm.create_plan(inquiry, context, self.tools.keys())
        
        for step in plan.steps:
            if step.requires_tool:
                result = self.tools[step.tool_name].execute(step.parameters)
                step.result = result
        
        response = self.llm.synthesize_response(plan, inquiry)
        self.memory.store_interaction(customer_id, inquiry, response)
        return response

What Are AI Agents? - Core Definition

Technical Definition