KOJIE Visual Agent Builder
Build powerful AI agents in minutes with drag-and-drop simplicity
Build Enterprise AI Workflows
Start with a template, or drag AI models from the sidebar to create custom workflows
Components
Agent Society Orchestration
Multi-agent coordination with persistent memory & task delegationEnterprise Integrations Marketplace
7,000+ business app connectors with real-time webhooks & OAuth 2.0Advanced Execution Engine
Sandboxed code execution, browser automation & real-time debugging๐ฏ MASTERMIND CANVAS ONLINE! ๐ฏ
โจ NEURAL STAGE ACTIVATED! โจ
NEURAL INTERFACE GUIDE
Or choose one of these options to get started quickly:
Use Template
Start with a ready-made agent
Drag & Drop
Drag components from the left panel
Properties
Select a node to view its properties
Step-by-Step Visual Agent Builder Guide
Master the neural workspace in 5 simple steps
Select Your AI Components
What to do: Browse the left component panel and find the AI agents you need.
What you'll see: Components organized by category (AI Agents, Data Tools, Logic, etc.)
Example Components:
- ๐ GPT-5 Text Generator - For content creation
- ๐ง Claude-4 Analyzer - For data analysis
- ๐ Gemini Vision - For image processing
- ๐ Data Processor - For data transformation
Tip: Hover over components to see their description and capabilities
Drag Components to Canvas
What to do: Click and drag your chosen components into the neural workspace canvas.
What you'll see: A glowing drop zone with neural grid that pulses when you hover over it
Expected Experience:
- โจ Drag Start: Component glows and follows your cursor
- ๐ง Hover Canvas: "NEURAL WORKSPACE" pulses with cyan/green effects
- โก Drop Success: Component appears with connection points and glow effects
- ๐ฏ Grid Snap: Component automatically aligns to the neural grid
Tip: The canvas will show "Drop Zone" indicators and expand when you approach with a component
Connect Your Workflow
What to do: Click and drag from output points (right side) to input points (left side) of other components.
What you'll see: Connection points that glow and expand when you hover over them
Connection Process:
- ๐ต Output Point: Blue circle on the right side of components
- ๐ข Input Point: Green circle on the left side of components
- โก Connection Line: Animated curve that flows between connected points
- ๐จ Visual Feedback: Lines glow with neural energy when data flows through
Sample Workflow:
Real Workflow Output Example:

This is what your completed workflow looks like with real AI processing results
Tip: Invalid connections will show red warning indicators
Configure Component Properties
What to do: Click on any component to see its properties in the right panel.
What you'll see: A detailed properties panel with all configurable options
Typical Properties:
- ๐ง Model Settings: Choose GPT-5, Claude-4, Gemini, etc.
- โ๏ธ Parameters: Temperature, max tokens, system prompts
- ๐ Input/Output: Data format and validation rules
- ๐ฏ Behavior: Retry logic, error handling, timeouts
GPT-5 Configuration Example:
Tip: Properties automatically save as you type
Test & Deploy Your Agent
What to do: Use the toolbar buttons to test your workflow and deploy it.
What you'll see: Real-time execution with visual feedback and results
Available Actions:
- โถ๏ธ Run Flow: Execute your workflow with test data
- ๐พ Save Flow: Store your workflow for later use
- ๐ Deploy Agent: Make your agent live and accessible
- ๐ Generate Code: Export as Python/JavaScript code
What Happens When You Run:
Tip: Watch components glow with neural energy as your agent processes data
Popular Workflow Examples
๐ง Content Generator
Input Topic โ GPT-5 Generate โ Format Output โ Save Content
๐ Data Analyzer
Upload Data โ Claude-4 Analysis โ Gemini Visualization โ Export Report
๐จ Creative Assistant
User Input โ GPT-5 Ideas โ DALL-E Images โ Combine & Present
๐ Research Agent
Query โ Perplexity Search โ Summarize โ Generate Insights
Pro Tips for Success
Plan Your Flow
Start with the end goal and work backwards to create efficient workflows
Test Early & Often
Use the Run Flow button frequently to catch issues before deployment
Optimize Settings
Fine-tune model parameters for better performance and cost efficiency
Use Error Handling
Add fallback nodes to handle failures gracefully in production