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HarmonyOS Agent DSL Practical Battle: A Breakthrough in Native AI Application Development

As one of the first developers to connect to Harmony Agent DSL, they used it to achieve intelligent linkage of devices in smart home appliance projects.This article combines practical experience to share how Agent DSL lowers the threshold for AI development and achieves a leap from single function to multi-agent collaboration.

1. The ice-breaking journey of native AI development

1.1 Three major dilemmas of traditional development

  1. Frame fragmentation: You need to master multiple sets of frameworks such as TensorFlow Lite, ML Kit, etc.
  2. Difficult to cross-end adaptation: The AI ​​capabilities of mobile phones/watches/home appliances vary greatly, and the adaptation cost is high
  3. Real-time bottleneck: Cloud AI call latency is high, local model deployment is complex

1.2 Agent DSL breaking point

  • Unified Development Model: A set of DSL is suitable for all Hongmeng devices
  • Native Integration: Deeply integrating Hongmeng distributed capabilities
  • Low code threshold: AI logic is separated from business logic, and non-AI developers can also get started

2. Agent DSL core features practical combat

2.1 Minimalist definition of an agent 

// Weather intelligent body (get weather and intelligent recommendation)
agent WeatherAgent {
// Basic ability: obtain weather
    func getWeather(city: String) -> WeatherData {
// Call Hongmeng Weather Service
        let data = HmsWeatherService.fetch(city)
        return data
    }

// Intelligent capability: Recommended according to weather
    func getRecommendation(weather: WeatherData) -> String {
        if weather.temp > 30 {
Return "It is recommended to turn on the air conditioner"
        } else if weather.rainfall > 5 {
return "Remember to bring an umbrella"
        }
return "Please be the weather"
    }
}
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2.2 Multi-agent collaborative programming 

// Smart home central control intelligent body
agent HomeController {
    private let weatherAgent = WeatherAgent()
    private let acAgent = AirConditionerAgent()
    private let lightAgent = LightAgent()

    func autoAdjust() {
// 1. Get the weather
let weather = weatherAgent.getWeather("Beijing")
// 2. Coordinate the air conditioner
        if weather.temp > 28 {
            acAgent.setTemp(24)
        }
// 3. Turn on the lights when the weather is dark
        if weather.light < 300 {
            lightAgent.turnOn()
        }
    }
}
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2.3 Distributed Agent Communication 

// Cross-device collaboration (mobile phone and air conditioner)
agent MobileController {
    func controlAc(acId: String, temp: Int) {
// Distributed call to the air conditioner intelligent body
        let acAgent = AgentFinder.find("air_conditioner:\(acId)")
        acAgent.send(SetTemperature(temp: temp))
    }
}
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3. Practical cases: intelligent sleep system

3.1 Armor Architecture Design 

┌─────────────┐    ┌─────────────┐    ┌─────────────┐  
│ Sleep Monitoring Agent │───→│ Data Analysis Agent │───→│ Equipment Control Agent │
│ (Watch end) │ │ (Mobile end) │ │ (Home appliance end) │
└─────────────┘    └─────────────┘    └─────────────┘  
        ↑                ↑                ↑  
        └────────────────┼────────────────┘  
                 ┌──────────────────────┐  
│ Distributed Message Bus │
                 └──────────────────────┘
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3.2 Core Code Implementation 

// Sleep monitoring intelligent body (watch end)
agent SleepMonitor {
    func monitor() -> SleepData {
// Read bracelet sensor data
        let data = WearableSensor.read()
        return data
    }
}

// Data analysis intelligent body (mobile terminal)
agent SleepAnalyzer {
    func analyze(data: SleepData) -> AdjustmentPlan {
// AI analyzes sleep quality
        if data.deepSleep < 1.5.hours {
            return Plan(light: 300, temp: 26)
        }
        return Plan(light: 200, temp: 24)
    }
}

// Equipment control intelligent body (home appliance terminal)
agent DeviceController {
    func execute(plan: AdjustmentPlan) {
        Light.setBrightness(plan.light)
        AC.setTemperature(plan.temp)
    }
}
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4. Performance optimization and best practices

4.1 Optimization strategy for resource-constrained devices

  1. Model lightweight: Use Hongmeng Lightweight AI compiler to optimize model size
  2. Local priority: Common models are cached locally in the device, reducing cloud calls
  3. Async processing: Use async to avoid blocking the UI by using non-real-time tasks

4.2 Pit avoidance guide

  1. Communication overhead: Reduce frequent communication between agents and merge batch messages
  2. Status Management: Avoid excessive maintenance of agents, and give priority to stateless design
  3. ** Version compatible**: When the agent interface is changed, it remains backward compatible

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