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SameX
SameX

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HarmonyOS native AI development: Agent DSL from Beginner to Mastery

As the first developer to use Agent DSL to develop Hongmeng AI applications, he used it in intelligent customer service projects to shorten the development cycle by 40%.This article uses basic syntax to distributed collaboration to systematically analyze the core capabilities of Agent DSL, helping developers quickly master the key technologies of native AI development.

1. The road to breaking the deadlock in AI native development

1.1 Three major pain points in traditional AI development

  1. The technical threshold is high: You need to master the underlying technologies such as TensorFlow Lite and model quantification.
  2. Difficult to cross-end adaptation: The AI ​​capabilities of mobile phones/watches/home appliances vary greatly, and the adaptation cost is high
  3. Insufficient real-time: Cloud call latency is high, local model deployment is complex

1.2 Agent DSL solution

  • Low code development: Through DSL abstraction of AI capabilities, non-AI developers can also quickly get started
  • Native integration: Deeply integrate Hongmeng distributed capabilities, deploy multi-end one-click
  • Real-time response: Local priority computing, reducing cloud dependencies

2. Detailed explanation of Agent DSL core syntax

2.1 The golden combination of @agent and @prompt 

// Definition of intelligent Q&A Agent
@agent class QAAgent {
// Prompt word engineering core: Defining Agent behavior pattern
    @prompt[
pattern = "Smart Q&A",
context = "Based on Hongmeng Equipment Knowledge Base",
examples = ["Q: 'How to take screenshot' → A: 'Swipe down from the top of the screen to call out the control center and click on the screenshot button'"]
    ]
    func answer(question: String) -> String {
// Call the local knowledge base or cloud API
        return knowledgeBase.query(question)
    }
}

//How to use
func main() {
    let agent = QAAgent()
let response = agent.answer("How to connect to Bluetooth device?")
    print(response)
}
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@prompt parameter analysis:

  • pattern: Define Agent behavior patterns and guide AI optimization response
  • context: Provide background knowledge and improve answer accuracy
  • examples: Input and output examples, specification response format

2.2 Multi-Agent Streaming Collaboration 

// Data collection Agent
@agent class DataCollector {
@prompt[pattern = "Collect device data"]
    func gather(deviceId: String) -> DeviceData {
// Get data from the device
        return deviceManager.fetch(deviceId)
    }
}

// Data Analysis Agent
@agent class DataAnalyzer {
@prompt[pattern = "Analyze device data"]
    func analyze(data: DeviceData) -> AnalysisResult {
// Analyze the data and generate a report
        return aiModel.process(data)
    }
}

// Streaming collaborative example
func main() {
    let collector = DataCollector()
    let analyzer = DataAnalyzer()

// Get multiple device data in parallel
    let [data1, data2] = awaitAll([
        async { collector.gather("device001") },
        async { collector.gather("device002") }
    ])

// Streaming data
    let result1 = analyzer.analyze(data1)
    let result2 = analyzer.analyze(data2)

// Merge results
    let report = generateReport([result1, result2])
}
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3. Intelligent tool chain practical combat

3.1 AI-assisted code generation

Enter natural language to generate Agent code in the IDE:

Enter: "Create a smart weather agent that can return 3-day weather forecast based on location"
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@agent class WeatherAgent {
    @prompt[
pattern = "weather query",
context = "Based on Hongmeng Weather Service API",
examples = ["Input: 'Beijing' → Output: 'Next 3 days: sunny, 18-25℃'"]
    ]
    func getForecast(location: String) -> String {
// API call logic generated by AI
        let response = weatherService.fetch(location, days: 3)
        return parseResponse(response)
    }
}
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3.2 Performance Tuning Tool

Use the Hongmeng AI Tuning Plugin to analyze the Agent:

graph TD
A[Agent call chain analysis] --> B{performance bottleneck}
B -->|Message Delay|C[Optimize Network Request]
B -->|Slow model reasoning|D[Local model quantization]
C --> E[Add cache policy]
D --> F[Model compression 40%]
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Previous and post-optimization:
| Indicators | Before Optimization | After Optimization | Improvement |
|------------|--------|--------|--------|

| Response Time | 800ms | 350ms | 56% |
| Model Size | 12MB | 7MB | 42% |
| Equipment Energy Consumption | High | Medium | 30% |

4. Distributed Agent Practical Battle

4.1 Cross-device collaboration case 

┌─────────────┐    ┌─────────────┐    ┌─────────────┐  
│ Mobile Agent │ │ Watch Agent │ │ Home Appliance Agent │
│ (User Interaction) │←→│ (Health Data) │←→│ (Device Control) │
└─────────────┘    └─────────────┘    └─────────────┘  
↑ Message bus ↑ Message bus ↑
        └────────────────┼────────────────┘  
                 ┌──────────────────────┐  
│ Distributed Message Middleware │
                 └──────────────────────┘
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4.2 Cross-device collaborative code 

// Mobile Agent (user interaction)
@agent class PhoneAgent {
    func getUserHealthData() {
// Call the watch Agent to obtain health data
        let watchAgent = AgentFinder.find("watch:healthAgent")
        let healthData = watchAgent.ask(GetHealthData())

// Call home appliance agent to adjust equipment
        let homeAgent = AgentFinder.find("home:deviceAgent")
        homeAgent.send(AdjustDevice(healthData))
    }
}

// Watch Agent (health data)
@agent class WatchAgent {
    receiver func GetHealthData() -> HealthData {
        return sensorManager.read()
    }
}
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5. Best Practices and Pit Avoidance Guide

5.1 Best Practices for Prompt Word Engineering

Inefficient Prompt Word:

@prompt["Answer user questions"]
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Efficient prompt words:

@prompt[
pattern = "Smart Home Control Q&A",
context = "Based on Hongmeng Device Control API",
    examples = [
"Q: 'Open the living room light' → A: 'The living room light has been turned on for you'",
"Q: 'Air conditioner set to 26 degrees' → A: 'The air conditioner temperature has been set to 26 degrees'"
    ]
]
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5.2 Common Traps and Solutions

  1. Newstorm:
  2. Problem: High-frequency communication of multiple agents causes system stuttering

  3. Solution: Add message anti-shake mechanism and merge similar messages

  4. Status consistency:

  5. Problem: Cross-device Agent status is not synchronized

  6. Solution: Use Hongmeng distributed data service to synchronize the status

  7. Model update delay:

  8. Problem: The Agent response remains unchanged after the AI ​​model is updated

  9. Solution: Implement the model hot loading mechanism

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