AI Agents Explained: What They Are & How to Build One

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Complete guide to AI agents in 2026

Complete guide • 9 min read

TL;DR

AI agents are language models that can plan, use tools, and take multi-step actions to complete tasks — instead of just responding to one prompt. Think of them as autonomous workers vs. chatbots.

What Is an AI Agent?

An AI agent is an LLM equipped with:

Tools: APIs, functions, or capabilities it can call (browse web, run code, query databases)
Memory: State that persists across interactions
Planning: Ability to break a goal into steps
Loop: Executes → observes → adjusts → repeats

Agents vs Chatbots

ChatbotAgent
Single response per promptMulti-step autonomous execution
No tool accessCalls APIs, runs code, browses
StatelessMaintains memory across steps
User in controlAgent plans its own next steps

Real Examples (2026)

Coding Agents

Claude Code — reads/edits your codebase, runs tests, iterates until working
Cursor Composer — multi-file edits in IDE
Devin (Cognition) — autonomous software engineer

Research Agents

OpenAI Deep Research — long-form research reports
Perplexity Pages — automated topic exploration

Computer-Use Agents

OpenAI Operator — clicks and types on websites
Anthropic Computer Use — screenshots and controls a virtual computer

Data Agents

ChatGPT Code Interpreter — analyzes CSVs, generates charts
Claude analysis tool — runs Python on your data

How Agents Actually Work

The typical loop:

User gives goal: "Book me a hotel in Berlin under $150 for next weekend"
Agent plans: Search hotels → filter price → check dates → book
Agent calls tool 1: Search API → returns hotels
Agent evaluates: Are any under $150? Which best matches?
Agent calls tool 2: Booking API → confirms
Agent reports: "Booked Hotel X, confirmation code..."

Building Your Own Agent

Frameworks

LangGraph (LangChain) — graph-based agent orchestration
AutoGen (Microsoft) — multi-agent conversations
CrewAI — role-based team of agents
Anthropic Agent SDK — direct tool_use API
OpenAI Assistants API — hosted agent runtime

Minimum Agent (Python)

`import anthropic

client = anthropic.Anthropic()

tools = [{
"name": "get_weather",
"description": "Get weather for a city",
"input_schema": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"]
}
}]

def run_agent(user_message):
messages = [{"role": "user", "content": user_message}]
while True:
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
tools=tools,
messages=messages
)
if response.stop_reason == "end_turn":
return response.content[0].text
# Handle tool use
for block in response.content:
if block.type == "tool_use":
result = my_tool_impl(block.name, block.input)
messages.append({"role": "assistant", "content": response.content})
messages.append({"role": "user", "content": [{
"type": "tool_result",
"tool_use_id": block.id,
"content": result
}]})`

Where Agents Fail

Long-horizon tasks: Beyond 15-20 steps, error compounds
Ambiguous goals: "Make my code better" → too vague
Rare edge cases: Web forms with unusual layouts
Cost: Agents make many LLM calls; watch the bill
Safety: Agents can take actions with real consequences (deleting files, sending emails)

The Future

2026 is the year agents move from "cool demos" to "production tools." Expect: better planning, cheaper models, more integrations, and gradual autonomy. But full "AGI as agent" is not here yet.

Related: What is AI? · Claude API Guide · AI Glossary