---
title: AI Agents Made Simple with VoltAgent
slug: ai-agent-voltagent
authors: necatiozmen
tags: [ai-agents, voltagent]
description: The evolution of AI agents, framework choices, and production challenges. Modern agent development with VoltAgent and VoltOps.
image: https://cdn.voltagent.dev/2025-09-05-agents-brief/social.png
---

## Introduction

AI agents are everywhere these days.

They're not just basic scripts anymore. Today's agents can handle tough problems we couldn't automate before. They're changing how we work. From customer support to writing code.

But here's what keeps developers up at night:

- Which framework actually works?
- How do we make agents that don't break?
- How do we see what they're doing?

## Agent Framework Choices and Architecture

There are two main ways to build agents. Some use a basic "think act observe" loop. Others use graph based structures for more control.

![ai agents ](https://cdn.voltagent.dev/2025-09-05-agents-brief/1.png)

### Where VoltAgent Fits In

[**VoltAgent**](https://github.com/VoltAgent/voltagent) works with both ways. Built in TypeScript, it catches mistakes before they hit production.

You can build:

- Basic chat agents
- Multi agent systems that work together
- RAG agents that search your data

### The Power of Workflows

Graph based agents check every step. Important when mistakes cost money. VoltAgent's workflow system lets you:

- Conditional execution with `.andWhen()`
- Parallel processing with `.andAll()`
- Race conditions with `.andRace()`

LLMs decide what happens next at each step. As models get smarter, we might go back to simpler designs. Pick a framework that works now and later.

## Common Mistakes When Building Agents

Everyone makes these mistakes. Here's how to avoid them.

#### Over Engineering from Day One

Start simple. Don't build a complex multi agent system when a single agent will do. Most teams waste months on fancy architectures they don't need. Get something working first, then add complexity.

#### Ignoring Error Handling

Agents fail in weird ways. LLMs timeout. APIs go down. Context windows overflow. Build retry logic, fallbacks, and graceful degradation from the start. Log everything. You'll need it when debugging at 3 AM.

#### Poor Prompt Engineering

Bad prompts kill agent performance. Too vague and agents hallucinate. Too specific and they can't adapt. Test prompts with edge cases. Version control them. A/B test in production. Small prompt changes can 10x your results.

#### No Human in the Loop

Pure automation sounds great until agents start doing damage. Build approval workflows for critical actions. Add confidence scores. Let humans override decisions. Systems should augment humans, not replace them.

#### Skipping Evaluation

How do you know if your agent works? Most teams launch without proper testing. Build eval datasets. Track success metrics. Run regression tests when you change prompts. Without evals, you're flying blind.

#### Context Window Management

Agents need memory but context windows are limited. Stuffing everything into context makes agents slow and expensive. Build smart retrieval. Summarize old conversations. Delete irrelevant info. Good context management cuts costs by 90%.

#### Security as an Afterthought

Agents can leak data, execute harmful code, or get prompt injected. Sanitize inputs. Limit tool access. Run in sandboxes. One compromised agent can wreck your whole system.

#### Wrong Model for the Job

GPT-4 for everything is expensive and slow. Use smaller models for simple tasks. Fine tune for specific domains. Mix models in pipelines. The right model strategy saves money and improves speed.

## Production and Observability

Real world agents can't just "work". They need to work all the time. API + queue setups handle tons of requests without dropping any.

**VoltOps** is built just for watching AI agents. See traces, metrics, and logs in real time. Works with OpenTelemetry, so you can track everything your agents do.

![ai agents 2 ](https://cdn.voltagent.dev/2025-09-05-agents-brief/2.png)

Track costs as they happen. See which prompts ran, token counts, and how long things took. All in one dashboard.

Testing agents is huge. VoltAgent uses **Viteval** to check if agents work right. Test for accuracy, catch hallucinations, check if answers make sense. All automatic.

## Ecosystem and Integrations

You need lots of tools to build agents. **VoltAgent** works with everything:

### LLM Provider Support

Works with all the big LLM providers:

- **OpenAI** (GPT-4, GPT-3.5)
- **Anthropic** (Claude 3 family)
- **Google AI** (Gemini models)
- **Groq** (for fast inference)
- **[Vercel AI SDK](https://voltagent.dev/docs/providers/vercel-ai)** with 30+ provider support

Switch providers with one line of code. No lock in. [View all providers →](https://voltagent.dev/docs/getting-started/providers-models)

### Vector Database Integrations

For [RAG (Retrieval Augmented Generation)](https://voltagent.dev/docs/rag/overview) applications:

- **[Pinecone](https://voltagent.dev/docs/rag/pinecone)**: Cloud based, high performance
- **[Chroma](https://voltagent.dev/docs/rag/chroma)**: Open source, self hosted option
- **[Qdrant](https://voltagent.dev/docs/rag/qdrant)**: Rust based, fast and reliable

### Modern Framework Support

- **[Next.js](https://voltagent.dev/docs/integrations/nextjs)** integration for full stack AI applications
- **[MCP (Model Context Protocol)](https://voltagent.dev/docs/agents/mcp)** support for extensible tools

### Memory and Storage Options

For different deployment scenarios:

- **[LibSQL](https://voltagent.dev/docs/agents/memory/libsql)**: Default for local development
- **[PostgreSQL](https://voltagent.dev/docs/agents/memory/postgres)**: Production ready, scalable
- **[Supabase](https://voltagent.dev/docs/agents/memory/supabase)**: Serverless with real time features
- **[InMemory](https://voltagent.dev/docs/agents/memory/in-memory)**: For test environments

Build fast prototypes or production systems. VoltAgent handles both.

## Industry Dynamics

Big tech companies are fighting for the agent space. Everyone's building the same stuff. Easy to get stuck with one vendor.

**VoltAgent and VoltOps** stay open. VoltAgent is MIT licensed. Totally free. VoltOps runs in the cloud or on your servers. Your data stays yours. Big companies love this.

Open source keeps you flexible. VoltAgent works with many providers, so switching models is easy. Ready for whatever comes next.

## Conclusion

AI agents will change everything. But you need more than just powerful models. Pick a framework that fits. Build solid infrastructure. Watch what's happening. Voice and text agents are different, but both need to work every time.

Models will improve and agents might get simpler. But right now, you need solid engineering to succeed.

## Practical Example: Your First Agent with VoltAgent

Let's create a simple customer support agent:

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

// Define a customer support agent
const supportAgent = new Agent({
  name: "Customer Support",
  instructions: "You are a helpful customer support agent.",
  llm: new VercelAIProvider(),
  model: openai("gpt-4o-mini"),
});

// Run the agent
const response = await supportAgent.generateText(
  "What is the status of my order? Order number: 12345"
);

console.log(response.text);
// "I'm checking your order. Order number 12345..."
```

That's it. Add VoltOps and you'll see token usage, response times, and costs right away.

Want to build agents that work? Move fast but stay flexible. VoltAgent and VoltOps do both.
