Build Multi-Agent AI Systems
An interactive guide to creating powerful, autonomous AI teams using low-code and no-code platforms. Go from concept to a working system without writing complex code.
System Architecture
This section explains the fundamental components of a multi-agent system. Click on each block in the diagram below to learn about its role and function in coordinating tasks and achieving complex goals.
Orchestrator
The "Project Manager"
Research Agent
Gathers Information
Writing Agent
Drafts Content
Reviewer Agent
Checks for Quality
External Tools
APIs, Databases
Click a component
Select a block from the diagram to see a detailed explanation of its function and importance within the system.
The 6-Step Building Process
Follow this structured process to design, build, and deploy your multi-agent system. Each step builds upon the last, ensuring a robust and effective final product.
Define the Project Goal
Clearly articulate the overall objective. What complex task do you want to automate? Break this goal down into smaller, manageable sub-tasks. Then, define the roles for your AI agents. For example, for an "automated market report," you might need a 'Data Researcher', a 'Trend Analyst', and a 'Report Writer'.
Example: Agent Roles
- Researcher Scans news APIs and web sources.
- Analyst Identifies patterns in the collected data.
- Writer Compiles findings into a coherent report.
Agent Implementation Simulator
Define an agent's core instruction (prompt) to see how it's structured.
Design, Implement & Orchestrate
Choose your no-code platform (see Tools section below). Design the system architecture, defining how agents communicate. Implement each agent by giving it a clear role, instructions (prompt), and access to necessary tools (like a web search API). Finally, create the orchestration logic—the workflow that dictates the sequence of agent actions and handoffs.
Test, Iterate & Deploy
Thoroughly test the entire workflow. Observe how agents interact and handle failures. Are there communication breakdowns? Is the final output accurate? Use these observations to refine agent prompts, adjust the orchestration logic, and improve error handling. Once the system is reliable, deploy it for its intended use.
Iteration Checklist
- ✓ Is the final output consistently meeting quality standards?
- ✓ How does the system handle unexpected inputs or tool failures?
- ✓ Is the flow of information between agents efficient?
Low-Code Tool Comparison
Choosing the right platform is crucial. This chart compares popular low-code/no-code tools based on their suitability for building multi-agent systems. Use this to guide your selection based on your project's needs.
Potential Use Cases
Multi-agent systems can tackle a wide range of complex tasks. Here are a few examples to inspire your own projects.
Automated Content Creation
An agent team researches a topic, drafts an article, finds relevant images, and formats it for publishing.
Personalized Trip Planning
Agents find flights, book hotels based on user preferences, and create a detailed daily itinerary.
Complex Data Analysis
A team of agents pulls data from multiple sources, cleans it, performs analysis, and generates a summary report with visualizations.
Software Development Helper
Agents can write boilerplate code, run tests, analyze errors, and suggest bug fixes, speeding up the development cycle.
Recruitment Automation
Agents can screen resumes, schedule interviews with candidates, and send follow-up communications.
Customer Support Triage
An initial agent understands a customer query and routes it to the correct specialized agent (e.g., billing, technical support).