Low-Code Multi-Agent Guide: Build AI Collaboration

Introduction: From Single AI to Team Collaboration

Remember the first time you used AI? A single AI that could answer questions, write articles, and analyze data. But as you used it more, you might have noticed that a single AI sometimes struggles with complex tasks—

• Ask it to write an in-depth article, and research and writing quality may suffer
• Ask it to analyze data, and it lacks professional visualization capabilities
• Ask it to handle complex tasks, and it often only excels in one aspect

This is where Multi-Agent comes in: instead of having one AI handle everything, let multiple specialized AIs work together, each handling what they do best.

The good news? With low-code platforms, you can build your own Multi-Agent system without writing a single line of code!

Part 1: What is Multi-Agent? A Simple Explanation

1.1 A Vivid Analogy

Imagine you're organizing a company annual party:

Traditional approach (Single Agent): You handle everything alone—finding a venue, planning the menu, creating presentations, sending invitations, decorating... Exhausting and impossible to do well.

Multi-Agent approach: You build a dedicated team—

• Venue Coordinator: Handles venue booking and setup
• Catering Specialist: Manages menu and food arrangements
• Design Expert: Creates invitations and presentations
• Communications Lead: Sends invitations and reminders
• Project Manager: Oversees everything and ensures smooth coordination

Each person focuses on their specialty, and together they deliver a perfect event.

Multi-Agent systems work the same way—multiple specialized AI agents, each with their own role, working together.

1.2 Core Advantages of Multi-Agent

1.3 Ideal Scenarios for Multi-Agent

✅ Content Creation Pipeline: Research Agent → Writing Agent → Review Agent → Formatting Agent

✅ Smart Customer Service: Intent Recognition Agent → Expert Answer Agent → Emotional Support Agent → Ticket Agent

✅ Data Analysis Assistant: Data Collection Agent → Cleaning Agent → Analysis Agent → Visualization Agent

✅ Office Automation: Email Agent → Calendar Agent → Meeting Notes Agent → Task Assignment Agent

Part 2: Low-Code Multi-Agent Platforms

Many excellent low-code platforms now make it easy for anyone to build Multi-Agent systems.

2.1 Platform Comparison

2.2 Why Tencent Cloud ADP?

As Tencent Cloud's enterprise-grade AI development platform, Tencent Cloud ADP (AI Development Platform) offers unique advantages in the Multi-Agent space:

2.3 Common Features of Low-Code Platforms

🎨 Visual Workflow Builder

• Drag-and-drop interface
• Flowchart-style design
• Real-time preview and debugging

🧩 Modular Components

• Pre-built agent templates
• Rich plugin marketplace
• One-click reuse of community creations

🔗 Easy Integrations

• No API development knowledge needed
• One-click connections to popular tools
• Webhook and automation support

📊 Operations & Monitoring

• Conversation logs and analytics
• User feedback collection
• Continuous optimization

Part 3: Multi-Agent Collaboration Patterns

In low-code platforms, Multi-Agent systems typically use these collaboration patterns:

3.1 Sequential Pattern (Pipeline)

How it works: Agent A completes its task, passes output to Agent B, and so on.

Typical scenario: Content creation pipeline

User inputs topic
    ↓
[Research Agent] → Gather relevant information
    ↓
[Outline Agent] → Create article structure
    ↓
[Writing Agent] → Draft the content
    ↓
[Polish Agent] → Improve language and flow
    ↓
[Review Agent] → Check facts and grammar
    ↓
Final article output

Configuration tips:

• Define clear role positioning and task descriptions for each agent
• Plan the agent execution sequence properly
• Use debugging features to verify output quality at each step

3.2 Parallel Pattern (Simultaneous Execution)

How it works: Multiple agents work simultaneously, results are combined at the end.

Typical scenario: Multi-dimensional analysis

        User asks: "Analyze this product"
                    ↓
    ┌───────────────┼───────────────┐
    ↓               ↓               ↓
[Market Agent]  [Tech Agent]   [User Agent]
Analyze trends  Analyze specs  Analyze reviews
    ↓               ↓               ↓
    └───────────────┼───────────────┘
                    ↓
            [Summary Agent]
         Combine all insights
                    ↓
        Output comprehensive report

Configuration tips:

• Split tasks appropriately for parallel execution
• Design clear result aggregation logic
• Handle timeouts for parallel tasks

3.3 Router Pattern (Smart Dispatch)

How it works: Based on user intent, intelligently route to the appropriate agent.

Typical scenario: Smart customer service

        User inquiry
            ↓
    [Intent Recognition Agent]
    Determine inquiry type
            ↓
    ┌───────┼───────┐
    ↓       ↓       ↓
  Pre-    Post-   Complaint
  sales   sales   Handling
    ↓       ↓       ↓
[Product] [Support] [Resolution]
 Agent     Agent     Agent

Configuration tips:

• Ensure accurate intent classification
• Set up fallback handling
• Support human handoff

3.4 Hierarchical Pattern (Manager-Worker)

How it works: Main agent breaks down and assigns tasks, worker agents execute.

Typical scenario: Complex project management

            User request
                ↓
        [Project Manager Agent]
        Understand and decompose tasks
                ↓
    ┌─────────┼─────────┐
    ↓         ↓         ↓
[Design]  [Development] [Testing]
 Agent      Agent        Agent
    ↓         ↓         ↓
    └─────────┼─────────┘
              ↓
      [Project Manager Agent]
      Review and deliver results

Part 4: Hands-On: Building a Content Creation Multi-Agent

Let's walk through a concrete example of building a Multi-Agent system on a low-code platform.

4.1 Requirements Analysis

Goal: Create an automated content creation assistant that can:

1. Research topics
2. Generate structured outlines
3. Write quality content
4. Suggest images
5. Provide SEO recommendations

4.2 Agent Role Design

4.3 Application Design

Step 1: Create a Multi-Agent Application

In Tencent Cloud ADP:

1. Log in to the platform console
2. Click "New Application"
3. Select "Multi-Agent Mode"

Step 2: Configure Each Agent

Research Expert Configuration Example:

Role Definition:
You are a senior content researcher, skilled at quickly 
gathering and organizing information.

Task Description:
Based on the user's topic, conduct comprehensive research 
and output:
1. Topic background
2. Core knowledge points (3-5)
3. Latest industry trends
4. Relevant data and case studies

Output Format:
Please output research results in structured markdown format.

Outline Planner Configuration Example:

Role Definition:
You are an experienced content strategist, skilled at 
designing engaging article structures.

Task Description:
Based on the research results, design a complete article 
outline including:
1. Compelling title (main + subtitle)
2. Article structure (3-5 main sections)
3. Key points for each section
4. Estimated word count distribution

Output Format:
Please output in clear hierarchical outline format.

Configuration examples for other Agents are not detailed here...

Step 3: Connect Agent Workflow

On the workflow canvas, connect agent nodes in sequential mode:

Start → Research Expert → Outline Planner → Content Writer → Visual Advisor → SEO Expert → End

Step 4: Debug and Publish

• Use built-in debugging to test each agent's output
• Verify smooth information flow between agents
• Once confirmed, click publish to go live

4.4 Testing and Optimization

Testing checklist:

1. Test with different topic types
2. Check output quality of each agent
3. Observe overall collaboration flow
4. Note areas needing improvement

Common optimization areas:

• Adjust prompts for better outputs
• Optimize context passing to reduce information loss
• Add conditional branches for special cases
• Set up retry mechanisms for stability

Part 5: Advanced Tips and Best Practices

5.1 Agent Prompt Optimization

Structured Prompt Template:

## Role Definition
You are [role name], with [core capabilities], 
specializing in [domain].

## Task Objective
[Clearly describe the task goal]

## Input Information
You will receive:
- [Input 1]: [Description]
- [Input 2]: [Description]

## Processing Steps
1. [Step 1]
2. [Step 2]
3. [Step 3]

## Output Requirements
- Format: [Specify format]
- Length: [Word count]
- Style: [Language style]

## Notes
- [Note 1]
- [Note 2]

## Example
[Provide an input/output example]

5.2 Common Issues and Solutions

5.3 Performance Optimization Tips

1. Use Caching Wisely

• Enable result caching for repeated queries
• Set appropriate cache expiration times

2. Async Processing

• Use async execution for non-critical paths
• Leverage parallel mode for efficiency

3. Tiered Processing

• Answer simple questions directly
• Only trigger Multi-Agent for complex tasks

4. Monitoring & Alerts

• Set up response time monitoring
• Configure exception notifications

Part 6: Real-World Case Studies

6.1 Case Study: E-commerce Customer Service

Background: An e-commerce platform with 100k+ daily inquiries, overwhelming human agents.

Solution:

• Intent Recognition Agent: Classify inquiry types
• Product Inquiry Agent: Answer product questions
• Logistics Agent: Handle order and shipping queries
• After-sales Agent: Process returns and exchanges
• Emotional Support Agent: Handle complaints

Results:

• Automation rate: 85%
• Average response time: <3 seconds
• Customer satisfaction: 92%

6.2 Case Study: Content Factory for Social Media

Background: A content team needs daily posts across multiple platforms.

Solution:

• Topic Agent: Recommend topics based on trends
• Research Agent: Gather materials and data
• Writing Agent: Generate drafts
• Adaptation Agent: Customize for different platforms
• Review Agent: Check content compliance

Results:

• 5x increase in content output
• Humans only needed for final review
• One-click multi-platform publishing

6.3 Case Study: Enterprise Knowledge Assistant

Background: Company knowledge scattered, employees struggle to find information.

Solution:

• Question Understanding Agent: Parse user queries
• Knowledge Retrieval Agent: Match answers from knowledge base
• Answer Generation Agent: Compose responses
• Follow-up Agent: Guide users to clarify needs
• Feedback Agent: Continuously improve knowledge base

Results:

• 80% improvement in knowledge discovery
• 50% reduction in new employee training time
• Significantly increased knowledge reuse

Part 7: Future Outlook

7.1 Multi-Agent Development Trends

Smarter Collaboration

• Agents autonomously negotiate and assign tasks
• Dynamic collaboration strategy adjustment
• Self-learning and optimization

Richer Capabilities

• Multimodal processing (text, image, voice, video)
• Real-time web access and tool usage
• Physical world interaction (IoT, robotics)

Easier Building

• Create agents with natural language descriptions
• Smart collaboration pattern recomxmendations
• One-click deployment and sharing

7.2 Quick Start Tips

1. Build your first Multi-Agent in 30 minutes: Start with platform templates for a quick hands-on experience
2. Explore application templates: Learn from enterprise-level examples and best practices
3. Iterate and optimize: Continuously adjust agent configurations based on business feedback
4. Stay updated: Keep track of new features and capability upgrades

Conclusion

Multi-Agent is becoming a core capability for enterprise digital transformation. With Tencent Cloud ADP, enterprises can rapidly build production-grade multi-agent applications and significantly improve business efficiency.

Whether it's intelligent customer service, content production, or business process automation, Multi-Agent delivers real value for enterprises.

Start now and build your first enterprise-grade Multi-Agent application!

Appendix: Quick Start Resources

Recommended Resources

• How Enterprises Build AI Agents
• Build a Customer Service AI Agent in 6 Steps
• Try Tencent Cloud ADP