What Is an AI Agent? Complete 2026 Guide

What makes an agent different from a chatbot?

An AI agent uses an LLM as its brain to make decisions, use tools, and execute actions autonomously to reach a goal. Unlike a simple chatbot that only answers questions, an agent can:

• Break complex tasks down into steps
• Use external tools (web search, APIs, databases)
• Remember the state of a task across steps
• Self-correct when a step fails
• Take real actions (sending emails, updating systems)

The ReAct loop: think and act

The most widely used pattern is ReAct (Reasoning + Acting):

1. Thought: the LLM reasons about the current state and what to do
2. Action: it decides which tool to use and with what parameters
3. Observation: it receives the tool's result
4. Repeat: it loops back to step 1 until the task is complete

Types of tools

Tools define an agent's superpowers:

• Search: Google, DuckDuckGo, document search
• APIs: CRM, ERP, ticketing systems, calendars
• Databases: SQL queries, vector search
• Code: running Python for complex calculations
• Files: reading PDFs, generating Excel reports
• Email/Messaging: sending emails, Slack messages

Enterprise use cases for agents

• Technical support: resolves tickets automatically, cutting those that reach humans by 60-70%
• Sales: analyzes leads, generates proposals, updates the CRM autonomously
• HR: onboarding, answering policy questions, scheduling meetings
• Financial analysis: extracts KPIs, detects anomalies, generates alerts
• Compliance: monitors regulations, generates audit reports

Code example

# An agent with tools using LangChain
from langchain_anthropic import ChatAnthropic
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.tools import tool
from langchain_core.prompts import ChatPromptTemplate

@tool
def lookup_customer(customer_id: str) -> str:
    # Queries the CRM for customer data
    return f"Customer {customer_id}: 3 purchases, average value $5,000"

@tool
def send_email(recipient: str, subject: str, body: str) -> str:
    # Sends an email to the recipient
    return f"Email sent to {recipient}"

llm = ChatAnthropic(model="claude-sonnet-4-6")
tools = [lookup_customer, send_email]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a sales agent. Use tools to complete tasks."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}")
])

agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
result = agent_executor.invoke({
    "input": "Look up customer C-001 and send them a personalized proposal"
})