How to Build an AI Agent from Scratch: Complete Developer Guide

Building an AI agent goes far beyond sending a prompt to a language model. An autonomous agent combines a reasoning LLM brain, memory systems, tool execution routines, and a self-correcting loop. This tutorial walks you through building a production-ready AI agent from scratch.

1. What Distinguishes an AI Agent from a Standard LLM Call?

A standard LLM call operates as a single-turn function: Input Prompt → Model → Text Output. In contrast, an AI Agent operates autonomously inside an execution loop:

2. The 4 Pillars of Agent Architecture

• Reasoning Layer (The Brain): The LLM (Claude Opus 5, DeepSeek-V3, GPT-5.6 Sol) that processes observations and generates structured function calls.
• Memory Subsystem: Short-term conversation history buffer combined with long-term vector search (ChromaDB / Pinecone) or knowledge graphs.
• Tool Execution Routines: Functions registered via JSON schema or Model Context Protocol (MCP) allowing the model to perform web searches, file edits, or terminal operations.
• Control Flow Pattern: The execution state machine—such as a ReAct (Reason + Act) loop, Plan-and-Solve framework, or LangGraph cyclic graph.

3. Complete Step-by-Step Python Implementation

Below is a clean, dependency-minimal Python script demonstrating a working ReAct agent loop:

import json
from openai import OpenAI

# Initialize client (works with DeepSeek, OpenAI, or local vLLM)
client = OpenAI(api_key="your-api-key")

# 1. Define Tool Functions
def calculate_budget(daily_cost: float, days: int) -> str:
    return json.dumps({"monthly_total": daily_cost * days})

tools_schema = [
    {
        "type": "function",
        "function": {
            "name": "calculate_budget",
            "description": "Calculate total monthly budget from daily spend",
            "parameters": {
                "type": "object",
                "properties": {
                    "daily_cost": {"type": "number"},
                    "days": {"type": "integer"}
                },
                "required": ["daily_cost", "days"]
            }
        }
    }
]

# 2. Agent Execution Loop
def run_agent_loop(prompt: str):
    messages = [{"role": "user", "content": prompt}]
    
    while True:
        response = client.chat.completions.create(
            model="deepseek-v3",
            messages=messages,
            tools=tools_schema
        )
        msg = response.choices[0].message
        
        # Check if model wants to invoke a tool
        if msg.tool_calls:
            tool_call = msg.tool_calls[0]
            args = json.loads(tool_call.function.arguments)
            print(f"Agent Action: Calling {tool_call.function.name} with {args}")
            
            # Execute tool locally
            result = calculate_budget(**args)
            
            # Append observation to history
            messages.append(msg)
            messages.append({"role": "tool", "tool_call_id": tool_call.id, "content": result})
        else:
            # Model finished reasoning
            print(f"Final Agent Answer: {msg.content}")
            break

run_agent_loop("If our API spend is $5.5 per day, what is our 30-day budget?")

4. Try the Interactive Studio

Ready to customize your agent architecture and generate TypeScript or Python boilerplate? Try our interactive AI Agent Builder Lab tool.