Adaptive AI Assistant Deployment
There is a growing need for flexible, user-centric AI assistant deployment on embedded devices. This challenge focuses on creating an adaptive multi-agent system that allows smart device manufacturers to seamlessly integrate and manage various AI assistants, while offering end-users granular control over their AI experience. Your solution will leverage Semantic Kernel for orchestrating AI skills and plugins, providing a robust framework for integrating Claude Opus 4.5 as a sophisticated conversational agent alongside smaller, locally-run models like Llama 3 for quick, device-side inference. Critical to this system is the implementation of MCP (Model Context Protocol) for secure, on-device tool integration, allowing AI agents to interact with TV functionalities, user preferences, and privacy settings. A2A Protocol will facilitate seamless communication between core system agents and the deployed AI assistants, ensuring a cohesive and responsive user experience even within resource-constrained environments, utilizing adaptive thinking budgets to optimize performance.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an adaptive multi-agent system using Semantic Kernel, Claude Opus 4.5, and MCP for user-controlled AI assistant deployment on smart devices.
How work is evaluated
The system will be evaluated on its ability to correctly process various user commands, seamlessly integrate different AI models (cloud/edge), manage device resources effectively, and demonstrate robust A2A communication and MCP tool usage. User experience scenarios will simulate typical smart TV interactions.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Implement skill orchestration using Semantic Kernel for diverse AI functionalities.
Apply adaptive thinking budgets to optimize AI performance on resource-constrained devices.
Architect a multi-agent system for dynamic AI assistant deployment on smart devices.
Design MCP for secure and granular access to device-specific tools and settings.
Integrate Claude Opus 4.1 and a local LLM for hybrid cloud/edge reasoning.
Utilize A2A Protocol for robust communication between system and assistant agents.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The system will be evaluated on its ability to correctly process various user commands, seamlessly integrate different AI models (cloud/edge), manage device resources effectively, and demonstrate robust A2A communicat...
Challenge input
{'user_command': 'str', 'device_state': {'volume': 'int', 'current_app': 'str'}}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'executed_actions': 'list_of_strings', 'response_text': 'str', 'models_used': 'list', 'resource_consumption_level': 'str'}
- All complex commands involving both cloud and edge models are executed without error.
- MCP-enabled tools for device control and preference management function correctly.
- Response Latency (Ms) target: 250
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- All complex commands involving both cloud and edge models are executed without error.
- MCP-enabled tools for device control and preference management function correctly.
Metrics
- Response Latency (ms) · Target 250
- Resource Utilization (CPU/RAM) · Target 0.5
Workflow tasks
- Task 1
Input: {'user_command': 'str', 'device_state': {'volume': 'int', 'current_app': 'str'}}
Output: {'executed_actions': 'list_of_strings', 'response_text': 'str', 'models_used': 'list', 'resource_consumption_level': 'str'}
Execution harness: Python
- Task 2
Input: {'user_id': 'str', 'action': 'str', 'preference_data': {'genre': 'str', 'value': 'str'} | None}
Output: {'status': 'str', 'updated_preferences': 'dict' | None}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.