Workflows Dashboard Tutorials

Master the AI-powered workflow automation platform. Learn to build agents, create workflows, and leverage RAG for intelligent applications.

Next.js 15 TypeScript Tailwind CSS Socket.IO AI Agents
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Quick Start Guide

1

Access Dashboard

Navigate to the dashboard and explore the main interface. Familiarize yourself with the navigation and feature sections.

2

Create an Agent

Build your first AI agent using the agent builder. Configure prompts, tools, and knowledge sources.

3

Build a Workflow

Design an automation workflow connecting agents, tools, and conditions in the visual editor.

4

Execute & Monitor

Run your workflow and monitor execution in real-time. Debug issues and optimize performance.

AI Agents

Discover, deploy, and manage intelligent AI agents. Browse the agent marketplace for pre-built templates or create custom agents for your specific needs.

Overview

AI Agents are autonomous entities that can perform tasks, answer questions, and interact with external systems. Each agent has configurable prompts, tools, and knowledge sources.

🧠 System Prompts

Define agent personality, behavior, and response patterns through customizable system prompts.

🔧 Tool Integration

Connect agents to external APIs, databases, and services through the tool registry.

📚 Knowledge Bases

Link RAG collections to provide agents with domain-specific knowledge.

Creating an Agent

  1. Navigate to Agents - Click "Agents" in the sidebar to access the agent management page.
  2. Click "New Agent" - This opens the agent configuration form.
  3. Configure Basic Settings - Name your agent and add a description.
  4. Set System Prompt - Define the agent's behavior and personality.
  5. Assign Tools - Select tools from the registry to extend capabilities.
  6. Link Knowledge - Connect RAG collections for domain expertise.
  7. Save & Deploy - Your agent is now ready for use!

API Example

TypeScript
// Create a new agent via API
const response = await fetch('/api/agents', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    name: 'Customer Support Agent',
    description: 'Handles customer inquiries',
    systemPrompt: `You are a helpful customer support agent.
      Be polite, professional, and solution-oriented.`,
    model: 'qwen-72b',
    tools: ['web_search', 'ticket_system'],
    knowledgeBase: 'product_docs'
  })
}