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Challenge

Audio-First Hybrid Reasoning Personal AI

This challenge tasks you with developing an audio-first personal AI assistant framework. The assistant must leverage multi-modal capabilities, primarily through advanced audio processing (using OpenAI latest or similar) and sophisticated natural language understanding from Claude Opus 4.5. The core innovation lies in implementing a hybrid reasoning system, switching seamlessly between 'instant' mode for quick, factual responses and 'deep' mode for complex requests requiring extensive research, planning, or multi-step logical inference. Participants will build a role-based agent team using CrewAI, where specialized agents handle different aspects of interaction (e.g., a 'Listener' agent, a 'Researcher' agent, a 'Scheduler' agent). The system will integrate MCP for secure access to user's personal data (e.g., calendar, email) and external APIs (e.g., weather, news). Utilizing Spur IQ (AI Engineering Tooling), developers will focus on robust development practices, testing, and deployment to ensure a highly responsive, context-aware, and ethically compliant personal AI experience.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop an audio-first personal AI assistant using CrewAI, Claude Opus 4.5, OpenAI 5.2, and hybrid reasoning, leveraging MCP for tool integration.

Delivery guide

How work is evaluated

Evaluation

Evaluation will focus on the assistant's ability to accurately interpret audio commands, effectively utilize hybrid reasoning, and securely integrate with MCP-enabled tools. Performance metrics will assess response time and contextual accuracy.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Learning goals

What you should walk away with

  • Implement a multi-modal, audio-first personal AI assistant framework.

  • Design and orchestrate a role-based agent team using CrewAI.

  • Integrate MCP for secure, contextual access to user's personal data and external APIs.

  • Develop a hybrid instant/deep reasoning system for diverse query types.

  • Employ Spur IQ practices for robust AI engineering, testing, and deployment.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 2 tasks

Challenge setup

Evaluation will focus on the assistant's ability to accurately interpret audio commands, effectively utilize hybrid reasoning, and securely integrate with MCP-enabled tools. Performance metrics will assess response ti...

Challenge input

{'audio_query': 'string', 'query_type': 'instant|deep'}

Agent execution

No agent tool is listed for this challenge.

Evaluated output

{'response': 'string', 'reasoning_mode_used': 'string', 'tool_calls': ['string']}

Evaluation checks
  • Ensures the agent selects the appropriate 'instant' or 'deep' reasoning mode for given queries.
  • Verifies successful and authorized invocation of MCP-enabled tools for personal data interaction.
  • Confirms the system correctly processes and understands audio input via OpenAI o3.
Configured evaluation evidence
  • ResponseContextualAccuracy target: 0.9
Configured runtime
  • 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

  • Ensures the agent selects the appropriate 'instant' or 'deep' reasoning mode for given queries.
  • Verifies successful and authorized invocation of MCP-enabled tools for personal data interaction.
  • Confirms the system correctly processes and understands audio input via OpenAI o3.

Metrics

  • ResponseContextualAccuracy · Target 0.9
  • ResponseLatency · Target 1000

Workflow tasks

  1. Task 1

    Input: {'audio_query': 'string', 'query_type': 'instant|deep'}

    Output: {'response': 'string', 'reasoning_mode_used': 'string', 'tool_calls': ['string']}

    Execution harness: Python

  2. Task 2

    Input: {'audio_command': 'string', 'personal_context': {'calendar_events': 'list', 'email_summary': 'string'}}

    Output: {'action_taken': 'string', 'tool_output': 'json', 'confirmation_required': 'boolean'}

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Audio-First Hybrid Reasoning Personal AI