What Is an AI Agent? Definition and Python Guide (2026)

An AI agent is a software program that uses a large language model (LLM) to perceive inputs, reason about them, and take actions — including calling external tools, writing code, or querying databases — in a loop until it achieves a goal. Unlike a single LLM call, an agent can complete multi-step tasks autonomously by deciding which actions to take based on intermediate results.

The term “agent” has been used broadly in AI research since the 1990s, but in 2024–2026 it has come to specifically mean LLM-powered programs that can use tools and maintain state across multiple steps.

The core loop

Every AI agent runs some version of this loop:

1. Perceive — receive a user message or environment observation
2. Reason — the LLM decides what to do next
3. Act — call a tool, write a file, query a database, or respond
4. Observe — get the result of the action
5. Repeat — or stop if the goal is met

The most widely used implementation of this loop is the ReAct pattern (Reason + Act), introduced by Yao et al. (2022). See What is a ReAct agent? for details.

What makes something an “agent” vs a chatbot

A chatbot responds to one message with one LLM call. An agent may make dozens of LLM calls and tool calls before returning an answer.

How to build an AI agent in Python

10xGraph provides a prebuilt ReactAgent that implements the full agent loop. You supply the tools; the framework handles routing, tool execution, and state.

from tenxgraph.prebuilt.agent import ReactAgent
from tenxgraph.core.state import AgentState, Message

def search_web(query: str) -> str:
    """Search the web and return a summary of results."""
    # Replace with a real search API call
    return f"Top results for '{query}': [result 1], [result 2]"

def get_weather(city: str) -> str:
    """Get current weather for a city."""
    return f"Weather in {city}: 22°C, sunny"

agent = ReactAgent(
    model="google/gemini-2.5-flash",
    system_prompt=[{"role": "system", "content": "You are a helpful assistant. Use tools when they help."}],
    tools=[search_web, get_weather],
)

app = agent.compile()

result = app.invoke(
    {"messages": [Message.text_message("What is the weather in Tokyo and latest AI news?")]},
    config={"thread_id": "demo-1"},
)
print(result["messages"][-1].text())

The agent will call get_weather for Tokyo, call search_web for the news query, then compose a final answer — all automatically.

Serving an AI agent as an API

Once you have a compiled agent, one command starts a production HTTP server:

pip install 10xscale-agentflow-cli
agentflow init
agentflow api

This gives you POST /v1/graph/invoke, POST /v1/graph/stream (SSE), and GET /v1/graph/threads/{id} — no extra FastAPI code required.

Types of AI agents

Why agents need more than a raw LLM call

A raw LLM call returns text. For production AI agents, you also need:

• Persistent threads — conversations that survive server restarts
• Tool execution — call functions, APIs, databases in parallel
• Streaming — send tokens to a frontend as they generate
• Auth and rate limiting — control who can call the agent and how often
• Observability — traces, metrics, error tracking

10xGraph ships all of these as part of the framework. See Get Started for the full stack.

Next steps