Build an AI Agent with API in 2026: Step-by-Step Tutorial

AI agents go beyond simple chat — they can use tools, make decisions, and execute multi-step tasks autonomously. In 2026, building a basic AI agent is surprisingly straightforward with the right API. This tutorial walks you through building one from scratch.

What Is an AI Agent?

An AI agent is a program that uses an LLM as its "brain" to:

• Understand goals: Parse what the user wants to accomplish
• Plan steps: Break complex tasks into smaller actions
• Use tools: Call functions, APIs, databases, or search the web
• Reason about results: Evaluate tool outputs and decide next steps
• Iterate: Loop until the task is complete or ask for clarification

Step 1: Define Your Tools

Tools are functions the agent can call. Define them as OpenAI-compatible tool schemas:

import json
import requests
from openai import OpenAI

client = OpenAI(
    base_url="https://api.aipower.me/v1",
    api_key="YOUR_AIPOWER_KEY",
)

# Define tools the agent can use
tools = [
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for current information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"},
                },
                "required": ["query"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "calculate",
            "description": "Perform a mathematical calculation",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {"type": "string", "description": "Math expression to evaluate"},
                },
                "required": ["expression"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "save_file",
            "description": "Save content to a file",
            "parameters": {
                "type": "object",
                "properties": {
                    "filename": {"type": "string"},
                    "content": {"type": "string"},
                },
                "required": ["filename", "content"],
            },
        },
    },
]

Step 2: Implement Tool Functions

def search_web(query):
    """Simulate web search (replace with real search API)."""
    return f"Search results for '{query}': [Top results would appear here]"

def calculate(expression):
    """Safely evaluate a math expression."""
    try:
        result = eval(expression, {"__builtins__": {}})
        return str(result)
    except Exception as e:
        return f"Error: {e}"

def save_file(filename, content):
    """Save content to a file."""
    with open(filename, "w") as f:
        f.write(content)
    return f"Saved to {filename}"

# Map function names to implementations
tool_functions = {
    "search_web": search_web,
    "calculate": calculate,
    "save_file": save_file,
}

Step 3: Build the Agent Loop

The agent loop is the core pattern: send a message, check if the model wants to call tools, execute tools, feed results back, and repeat.

def run_agent(user_task, max_steps=10):
    """Run an AI agent that can use tools to complete a task."""
    messages = [
        {
            "role": "system",
            "content": "You are a helpful AI agent. Break complex tasks into steps. "
                       "Use the available tools to gather information and take actions. "
                       "Think step by step before acting."
        },
        {"role": "user", "content": user_task},
    ]

    for step in range(max_steps):
        response = client.chat.completions.create(
            model="deepseek/deepseek-chat",  # Affordable for agent loops
            messages=messages,
            tools=tools,
            tool_choice="auto",
        )

        message = response.choices[0].message
        messages.append(message)

        # If no tool calls, the agent is done
        if not message.tool_calls:
            print(f"Agent finished: {message.content}")
            return message.content

        # Execute each tool call
        for tool_call in message.tool_calls:
            fn_name = tool_call.function.name
            fn_args = json.loads(tool_call.function.arguments)
            print(f"  Step {step + 1}: Calling {fn_name}({fn_args})")

            result = tool_functions[fn_name](**fn_args)

            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": result,
            })

    return "Agent reached max steps without completing."

# Run the agent
run_agent("Calculate the compound interest on $10,000 at 5% annual rate for 10 years")

Step 4: Add Memory and Context

class Agent:
    def __init__(self, model="deepseek/deepseek-chat"):
        self.model = model
        self.memory = []  # Long-term memory across conversations

    def run(self, task):
        context = "\n".join(self.memory[-5:])  # Last 5 memories
        messages = [
            {"role": "system", "content": f"Previous context:\n{context}"},
            {"role": "user", "content": task},
        ]
        result = run_agent_with_messages(messages, self.model)
        self.memory.append(f"Task: {task} -> Result: {result[:200]}")
        return result

Choosing the Right Model for Agents

Cost Optimization for Agents

Agents make multiple API calls per task (typically 3-10). Cost control is critical:

• Use cheap models for planning: Let DeepSeek V3 plan, then call Claude for execution
• Limit max steps: Set a hard cap to prevent runaway loops
• Cache tool results: Same web search should not hit the API twice
• Use max_tokens: Prevent verbose intermediate reasoning from inflating costs

Build your first AI agent today at aipower.me — 50 free API calls, all models support tool calling. One API key for GPT, Claude, DeepSeek, and 13 more.