"This ChatGPT thing is nice and all, but how do I make something I can actually use in real life?"

That's what's circulating in the heads of almost every developer these days. Building a simple chatbot is child's play nowadays, but useful, real-world AI applications? Yeah, that's a different ball game.

In this article, we will cover what LLM agents are, why they're popular in 2025, and most importantly, how you can build them. A full guide supplemented by real-world examples and code snippets.

What is LLM Agent and Why Do They Matter So Much?

Click to zoom

What is the main difference between a regular chatbot and an LLM agent?

Chatbot: "Hello, I can help you with that." gives you an answer, done.

LLM Agent: "Ah, to answer this question I need to make that API call first, then retrieve some data from this database, perform some computation. Okay, now I can give you an answer."

See the difference? Agents can think, reason, and most importantly, communicate with the outside world.

Key Difference

Chatbots are reactive (responsive), but agents are proactive (planned action systems). Agents are capable of making decisions independently and retrieving information from outside the system.

🏗️ Agent Architecture Explorer

Real-World Examples

I just created a customer service agent. This agent:

• Reads customer questions
• Retrieves customer data from the CRM system
• Opens tickets with the tech team when necessary
• Sends emails
• Even does simple tasks independently

Result? Customer satisfaction improved, our workload reduced. Win-win situation.

LLM Agent Architecture: How Does This Thing Work?

So what's going on inside an LLM agent? As it happens, it's very similar to the way the human brain operates.

First, there's the LLM brain — GPT, Claude, Gemini, whatever. That's the core of the agent. The part that thinks, gets it, makes decisions. But on its own, it's really not very useful because it can only generate text.

That is where tools come in. These are the feet and hands of the agent. API calls, database calls, file access, web scraping, computation. The agent interacts with the real world through these tools. "Let me call this API to see the weather" for example.

And then there's the memory system that's super critical. It wouldn't have anything to remember without it, so the agent starts fresh every time. "Who was it again, what was I discussing?" Memory enables it to remember previous conversations and track context.

And finally, there's the planning and orchestration mechanism. "In order to do this job, I need to do this first, then that, and if I make an error I need to deal with it this way." This is the chunk that enables this sort of thinking. This is actually the most complex chunk.

As you can see, even such a simple question goes through a lot of steps in the agent. Dealing with this orchestration process is really tough.

Modern Agents' Superpowers

Multi-Step Reasoning

They can break hard problems down into pieces. "In order to do this task, I must first do this, then that", that's what they do.

Tool Usage

APIs, databases, web services. They can talk to anything.

Multimodal Capabilities

Not only text, they can process voice, pictures, even video.

Structured Output

JSON, XML, custom formats. Anything you desire, they can spit it out.

⚡ Agent Capabilities Matrix

Here's the Problem: Why Is Building Agents So Hard?

Seriously, to start with it was hell. I had the following issues:

Main Challenges

Orchestration Complexity: How do you deal with when the agent calls up which tool?

Error Handling: What if an API call fails? Does the agent go mad?

Memory Management: How do you store conversations, how much back do you go?

Cost Optimization: Every tool call costs tokens, tokens cost money. How do you optimize this?

Debugging: How do you understand what the agent is thinking?

You usually have two options:

1. Rebuild everything from scratch — Full control, but slow and complex
2. Use no-code tools — Easy starting point, but limited and rigid

Fortunately, there is something better today.

VoltAgent: A Framework Built for Developers

That's exactly why we built VoltAgent. After struggling with these challenges for months, we realized developers needed something different: a solution that is flexible but not complex.

VoltAgent is our developer-focused AI agent toolkit. We designed it to provide the freedom of coding from scratch along with productivity by using pre-existing solutions.

Why Is It Different?

Modular Architecture:

• @voltagent/core — Core engine
• @voltagent/voice — Voice capability
• @voltagent/vercel-ai — Vercel AI support
• Add whatever modules you require, leave out what you don't

Provider Independent: OpenAI, Google, Anthropic, doesn't matter. If some other provider appears tomorrow, it's super easy to switch.

Developer Experience: Made for developers. IntelliSense, TypeScript support, easily readable documentation.

Practical Example

Simple Agent Example

We can create a simple agent and implement a robust AI assistant in three lines of code:

import { VoltAgent, Agent } from "@voltagent/core";
import { VercelAIProvider } from "@voltagent/vercel-ai";
import { openai } from "@ai-sdk/openai";

const agent = new Agent({
  name: "My Helper",
  instructions: "A friendly assistant. Gives clear and genuine answers to questions.",
  llm: new VercelAIProvider(),
  model: openai("gpt-4o"),
});

// Usage
const response = await agent.generateText("How's the weather today?");

Tool System

Our tool system is designed to be intuitive and powerful. Here's how it works:

import { createTool } from "@voltagent/core";
import { z } from "zod";

const weatherTool = createTool({
  name: "get_weather",
  description: "Get weather information for a specified city",
  parameters: z.object({
    city: z.string().describe("City name, e.g., New York"),
  }),
  execute: async ({ city }) => {
    // Real API call would go here
    return { temperature: "72°F", condition: "Sunny" };
  },
});

const agent = new Agent({
  // ... other config
  tools: [weatherTool],
});

Now the agent can respond with weather queries based on actual data!

Click to zoom

This flow demonstrates how our tool system orchestrates different components. We designed it to be simple and self-explanatory.

Memory Management

To make it remember conversations:

import { LibSQLStorage } from "@voltagent/core";

const memoryStorage = new LibSQLStorage({
  // configuration
});

const agent = new Agent({
  // ... other config
  memory: memoryStorage,
});

And voilà! Now the agent stores previous conversations.

Memory System

Memory is the most precious part of agents. It brings consistency to the user relationship and allows personalization.

Click to zoom

Without memory, each conversation must start all over again. Even if the user says "we talked about this yesterday," the agent would respond with "Who are you?" Terrible experience!

VoltAgent Console: Game Changer

We built VoltAgent Console to solve a critical problem in agent development. With it, you can visually inspect your agents:

• Preview conversation flows
• Debug calls to tools
• Track performance metrics
• Catch mistakes with ease

It's vital to understand what your agent is doing in production.

Important for Production

Operating an agent in production without VoltAgent Console is akin to driving blindfolded. Use it definitely for debugging and optimization.

Best Practices (From My Experience)

Critical Success Factors

These are practices acquired from my experience; guidelines you absolutely must follow to be successful with agent projects:

Define your agent's personality well.

Instead of generic statements like "be helpful," get concrete. "Be a patient, friendly assistant who gives complete explanations" is so much better. Paint the personality of the agent alive.

Choose tools wisely.

Steer clear of the trap of supplying tools for everyone. Include only the features that you really need. Too many tools confuse the agent, too few leave the agent wanting.

Never leave out error handling.

APIs can fail, network outages can happen, you can hit rate limits. The agent has to handle these situations gracefully. Otherwise, the user experience is terrible.

Monitor costs at all times.

Each tool call translates to tokens, tokens translate to money. You will be shocked when you get the bill if you release without monitoring. I have been a victim of this in the past.

Test, test, test!

Think about edge cases. Plan what happens when the agent encounters bizarre scenarios. Take your mind to the "What if the user does something idiotic?" place and experiment.

The Future: Where Is This Going?

Hint: It's Not One Agent:

Not just one agent, but agents talking to other agents. One researches, another analyzes, a third writes reports.

More Powerful Reasoning:

Agents will be able to solve more difficult problems and make longer-term planning.

Enterprise Integration:

Easier integration with ERPs, CRMs, internal applications.

Build Your First Agent: Step by Step

Practical Guide

Now let's apply theory to practice. Let's build a simple but useful agent, you can build a working agent in 15 minutes by following these steps:

1. Setup

npm create voltagent-app@latest my-first-agent
cd my-first-agent
npm install

2. Basic Agent

// src/agent.ts
import { Agent } from "@voltagent/core";
import { VercelAIProvider } from "@voltagent/vercel-ai";
import { openai } from "@ai-sdk/openai";

export const myAgent = new Agent({
  name: "My First Agent",
  instructions: `
    You are a helpful assistant. For users:
    - Give clear and understandable answers
    - Explain with examples
    - If you don't know something, say you don't know
  `,
  llm: new VercelAIProvider(),
  model: openai("gpt-4o"),
});

3. Test It

// test.ts
import { myAgent } from "./src/agent";

async function test() {
  const response = await myAgent.generateText("How can I filter arrays in JavaScript?");
  console.log(response.text);
}

test();

That's it! Your first agent is now up and running.

4. Add Tools

Let's add a weather tool:

import { createTool } from "@voltagent/core";
import { z } from "zod";

const weatherTool = createTool({
  name: "get_weather",
  description: "Get weather for a city",
  parameters: z.object({
    city: z.string(),
  }),
  execute: async ({ city }) => {
    // Simple mock data
    const weather = {
      "new york": "72°F, Sunny",
      chicago: "65°F, Cloudy",
      "los angeles": "78°F, Clear",
    };
    return weather[city.toLowerCase()] || "Information not found";
  },
});

// Add to agent
export const myAgent = new Agent({
  // ... previous config
  tools: [weatherTool],
});

And that's it! You've got a functional agent in 15 minutes. Actual projects are more complex, but this is the general idea.

Final Words

LLM agents are 2025's real game changer. Not just chatbots, but AI apps that can actually do real work.

Tools like VoltAgent make this process significantly simpler. Instead of coding from scratch, you can focus on the actual work.

Start today. Make a basic agent, test it, learn. This technology is evolving very fast, and early birders will be the ones who benefit. Agents are not a fad, but the future of software development.