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Challenge

AI-Powered Content Licensing & Preparation

This challenge focuses on building an advanced multi-agent system to automate the process of identifying, analyzing, and preparing educational content for AI model training. The system will use Claude Opus 4.1 for its superior long-context reasoning to analyze complex licensing agreements and content suitability, alongside OpenAI o3 for high-volume summarization and content generation. LlamaIndex will provide robust data indexing and retrieval capabilities for heterogeneous datasets (e.g., video transcripts, metadata, legal documents). The core innovation lies in implementing the MCP server for secure, verifiable licensing agreement checks and content usage permissions. Agents will collaborate to ingest raw content, extract key information, identify AI training potential, ensure compliance via MCP calls, and then format the data for ingestion by various AI models. This requires sophisticated tool integration with simulated licensing databases and content management systems.

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Challenge brief

What you are building

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

Develop a multi-agent system using Claude Opus 4.1 and LlamaIndex for AI content licensing, preparation, and MCP-enabled compliance verification.

Delivery guide

How work is evaluated

Evaluation

The system will be evaluated on its ability to correctly identify and process content suitable for AI training, accurately extract licensing terms, verify compliance via MCP, and generate correctly formatted datasets.

Datasets

Challenge datasets

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

Learning goals

What you should walk away with

  • Utilize Claude Opus 4.1 for deep contextual understanding of complex legal and scientific content.

  • Build a multi-agent system for content analysis and preparation for AI training.

  • Orchestrate agent workflows using Semantic Kernel for automated content processing pipelines.

  • Develop a robust RAG system with LlamaIndex for efficient data retrieval across diverse sources.

  • Implement Model Context Protocol (Model Context Protocol) for secure and verifiable licensing checks.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 3 tasks

Challenge setup

The system will be evaluated on its ability to correctly identify and process content suitable for AI training, accurately extract licensing terms, verify compliance via Model Context Protocol, and generate correctly...

Challenge input

{ 'video_id': 'v123', 'transcript': 'Long text transcript...', 'metadata': { 'topic': 'physics', 'duration': '10min' } }

Agent execution

No agent tool is listed for this challenge.

Evaluated output

{ 'video_id': 'v123', 'summary': 'Concise summary...', 'keywords': ['quantum mechanics', 'black holes'], 'ai_training_potential': 'high' }

Evaluation checks
  • All Model Context Protocol checks must correctly identify compliant/non-compliant content based on provided...
  • The final formatted dataset must adhere precisely to the specified target format.
Configured evaluation evidence
  • Content Summarization Quality (LLM Score) target: 4
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

  • All Model Context Protocol checks must correctly identify compliant/non-compliant content based on provided agreements.
  • The final formatted dataset must adhere precisely to the specified target format.

Metrics

  • Content Summarization Quality (LLM Score) · Target 4
  • Keyword Extraction Relevance · Target 90

Workflow tasks

  1. Task 1

    Input: { 'video_id': 'v123', 'transcript': 'Long text transcript...', 'metadata': { 'topic': 'physics', 'duration': '10min' } }

    Output: { 'video_id': 'v123', 'summary': 'Concise summary...', 'keywords': ['quantum mechanics', 'black holes'], 'ai_training_potential': 'high' }

    Execution harness: Python

  2. Task 2

    Input: { 'content_id': 'v123', 'proposed_use_case': 'fine-tuning text generation model', 'licensing_agreement_text': 'Legal document describing usage terms...' }

    Output: { 'content_id': 'v123', 'mcp_status': 'compliant|non-compliant', 'reason': 'Explanation of compliance status' }

    Execution harness: Python

  3. Task 3

    Input: { 'content_data': { 'summary': '...', 'keywords': [...], 'transcript': '...' }, 'target_format_spec': { 'fields': ['summary_text', 'tags'], 'delimiter': ';' } }

    Output: { 'formatted_data': 'summary_text: [summary]; tags: [tag1, tag2];' }

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about AI-Powered Content Licensing & Preparation