AI Application Layer
An AI Application Layer is an AI system layer that manages AI application components (to support AI system interaction through interface management and business logic integration).
- Context:
- It can typically handle User Requests through AI interfaces.
- It can typically process Business Logic through AI integrations.
- It can typically manage Service Orchestration through component coordinations.
- It can typically coordinate Model Interaction through service gateways.
- It can typically control System Access through authentication services.
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- It can often implement API Integration through service endpoints.
- It can often provide Response Processing through result formatters.
- It can often manage Service Discovery through registry systems.
- It can often support Load Distribution through balancer services.
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- It can range from being a Simple AI Interface to being a Complex AI Gateway, depending on its interaction complexity.
- It can range from being a Synchronous AI Service to being an Asynchronous AI Service, depending on its processing model.
- It can range from being a Standalone AI Application to being an Integrated AI Application, depending on its integration level.
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- It can integrate with AI Model Layer for inference services.
- It can connect to AI Data Layer for data access services.
- It can utilize AI Infrastructure Layer for resource managements.
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- Examples:
- AI Application Layer Groups for AI system groups, with components such as frontend system components and backend system components.
- AI User System Layers for AI interaction systems, with components such as interface system components and experience system components.
- AI Interface Layers for AI interaction systems, with components such as application gateway components and user interface components.
- AI Gateway System Layers for AI access systems, with components such as security system components and routing system components.
- AI API Gateway Layers for AI service access systems, with components such as service endpoint components and request router components.
- AI Integration System Layers for AI service systems, with components such as orchestration system components and messaging system components.
- AI Service Integration Layers for AI service coordination systems, with components such as service connector components and service registry components.
- AI User System Layers for AI interaction systems, with components such as interface system components and experience system components.
- AI Process Layer Groups for AI workflow groups, with components such as business process components and task management components.
- AI Business System Layers for AI business systems, with components such as logic system components and rule system components.
- AI Business Layers for AI business systems, with components such as workflow components and integration components.
- AI Task System Layers for AI process systems, with components such as task system components and workflow system components.
- AI Workflow Layers for AI process systems, with components such as task manager components and process orchestrator components.
- AI Event System Layers for AI event systems, with components such as event system components and trigger system components.
- AI Event Processing Layers for AI event handling systems, with components such as event handler components and event router components.
- AI Business System Layers for AI business systems, with components such as logic system components and rule system components.
- AI Operation Layer Groups for AI runtime groups, with components such as execution system components and resource system components.
- AI Execution System Layers for AI processing systems, with components such as runtime system components and inference system components.
- AI Processing Layers for AI computation systems, with components such as inference components and optimization components.
- AI Resource System Layers for AI infrastructure systems, with components such as compute system components and scaling system components.
- AI Resource Layers for AI resource systems, with components such as resource allocator components and scheduler components.
- AI Monitoring System Layers for AI observability systems, with components such as metric system components and logging system components.
- AI Monitoring Layers for AI observation systems, with components such as metric collector components and alert manager components.
- AI Execution System Layers for AI processing systems, with components such as runtime system components and inference system components.
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- AI Application Layer Groups for AI system groups, with components such as frontend system components and backend system components.
- Counter-Examples:
- AI Model Layer, which handles model execution rather than application logic.
- AI Data Layer, which manages data processing rather than service integration.
- AI Infrastructure Layer, which provides compute resources rather than business services.
- See: AI Service Pattern, Application Integration Pattern, Service Gateway Pattern, Business Logic Pattern.