The Chatbot Problem: Why Basic Agents Are Useless

Watch This Step Video

Why Most Chatbots Fail

You've probably interacted with dozens of chatbots. Most of them are frustrating, limited, and feel like talking to a very sophisticated answering machine. Here's why:

Traditional Chatbots

Pre-scripted responses only

Can't perform real actions

No memory between conversations

Break with unexpected inputs

AI Agents

Understand context and intent

Use tools to take real actions

Remember and learn from interactions

Handle complex, multi-step tasks

Real-World Example: Customer Support

Let's see the difference in action. Imagine a customer needs help with a billing issue:

What Users Want:

"Book a meeting room for 3pm"

"Check our website's status"

"Generate a sales report"

"Order lunch for the team"

"Deploy the new feature"

What Your Agent Says:

"I can't book rooms, but here's how..."

"I can't check websites, but you should..."

"I can't generate reports, but try..."

"I can't order food, but here are some..."

"I can't deploy code, but the process is..."

The Reality Check

After a week, users stop using your "AI assistant" because it's just a fancy search engine that can't actually assist with anything. Sound familiar?

The Solution: Give Your Agent Tools

Let's fix your useless chatbot by adding a real tool. Hover over the explanations to see how each part works.

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

const getWeatherTool = createTool({
name: "get_weather",
description: "Get current weather for any city",
parameters: z.object({
location: z.string().describe("City and state, e.g. New York, NY"),
}),
execute: async ({ location }) => {
// In production, you'd call a real weather API
console.log("Getting weather for " + location + "...");
// Simple demo logic
if (location.toLowerCase().includes("new york")) {
return { temperature: "18°C", condition: "Partly cloudy" };
}
return { temperature: "24°C", condition: "Sunny" };
}
});

const agent = new Agent({
name: "my-agent",
instructions: "A helpful assistant that can check weather",
llm: new VercelAIProvider(),
model: openai("gpt-4o-mini"),
tools: [getWeatherTool],
});

new VoltAgent({
agents: { agent },
});

"I need to define what the tool does"

createTool is your starting point. Give it a clear name and description so the LLM knows when to use it.

"What should I call it?"

Name and description tell the LLM what this tool does and when to use it. Be descriptive but concise.

"What inputs does it need?"

Use Zod schemas to define and validate inputs. This gives you type safety and runtime validation.

"What should it actually do?"

The execute function is where the magic happens. Call APIs, query databases, whatever you need.

"How do I give it to my agent?"

Add the tool to your agent's tools array. The agent will automatically understand when to use it.

The Magic

Your agent now takes action instead of giving advice. It calls your get_weather function automatically and provides real data. This is the power of tools.

Testing in VoltOps Console

Now let's test your tool-powered agent in the VoltOps console.

Step-by-Step Testing

Update your code with the tool (above) and save the file

Your agent will automatically reload with the new tool

Go back to VoltOps Console:

console.voltagent.dev

Try these inputs to see your tool in action:

What's the weather in New York?

Check weather in San Francisco

Is it sunny in Tokyo?

See Your Tool in Action

This is what happens when you ask your agent about weather:

VoltAgent Tool Demo - Weather tool in action

Your agent now executes tools automatically and provides real data

Debug & Monitor

In the VoltOps console, you'll see:

Tool execution logs

Agent reasoning process

Response time metrics

Error tracking

The Transformation: From Chatbot to Agent

Watch how your agent's behavior completely changes with just one tool.

Before (Useless Chatbot)

User:

"What's the weather in NYC?"

Agent:

"I can't check current weather data. Try checking weather.com or your local weather app."

After (Real Agent)

User:

"What's the weather in NYC?"

Agent:

"Let me check that for you... The current weather in New York is 18°C with partly cloudy conditions."

The Magic

Your agent now takes action instead of giving advice. It calls your get_weather function automatically and provides real data. This is the power of tools.