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roanbrasil
/
devops-gitops-corpus

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new Agent({
name: "Editor",
instructions: "Review and improve content",
}),
],
});
```
This creates an end-to-end content production process where each agent does a segment of the process.
## Things to Watch Out for in Orchestration
### 1. Error Handling
What happens if one of the agents breaks? In VoltAgent, you can utilize try-catch constructs to invoke fallback logic:
```ts
try {
const result = await supervisorAgent.generateText(prompt);
} catch (error) {
console.log("Agent orchestration error:", error);
// Fallback logic
}
```
### 2. Cost Management
Multiple agents can increase cost. You can track cost in the VoltOps dashboard.
### 3. Latency Optimization
For concurrent jobs, you can run agents in parallel:
```ts
// Parallel execution example
const [research, analysis] = await Promise.all([
researchAgent.generateText("Research Bitcoin"),
analysisAgent.generateText("Do crypto trend analysis"),
]);
```
## Monitoring and Debug
One of the strongest features of VoltAgent is observability. In the VoltOps dashboard:
- You see agent execution paths
- You track performance metrics
- You see error rates
- You optimize cost
```ts
// For detailed logging
const agent = new Agent({
// ... configuration
hooks: {
onStart: (context) => console.log("Agent started:", context),
onEnd: (result) => console.log("Agent completed:", result),
onError: (error) => console.log("Error occurred:", error),
},
});
```
## Conclusion
AI agent orchestration is the key to transitioning from simple chatbots to enterprise-grade systems. With VoltAgent:
- You stay in the TypeScript ecosystem
- You have visibility with graphical monitoring
- You build systems ready to scale with a modular architecture
- You simply handle complex workflows using the supervisor pattern
Start with a single agent, grow to orchestration on demand. With VoltAgent's subagent structure, you can grow without having to change your existing code.
Remember: The best orchestration is one the user will never even see. Have your agents orchestrate in the background, show the user the best result only.
<|endoftext|>
# source: VoltAgent__voltagent/website/blog/2025-05-26-llm-agents/index.md type: docs
---
title: What are LLM Agents?
description: How to develop real AI applications with LLM agents? We'll be looking at how agent frameworks work.
tags: [llm]
slug: llm-agents
image: https://cdn.voltagent.dev/2025-05-26-llm-agents/social.png
authors: omeraplak
---
import ZoomableMermaid from '@site/src/components/blog-widgets/ZoomableMermaid';
import AgentArchitectureExplorer from '@site/src/components/blog-widgets/AgentArchitectureExplorer';
import AgentCapabilitiesMatrix from '@site/src/components/blog-widgets/AgentCapabilitiesMatrix';
"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?
<ZoomableMermaid
chart={`