DSA Compliance Monitoring
This challenge focuses on developing an ethical multi-agent system to monitor and report on public data access for researchers under the Digital Services Act (DSA). The system will identify potential non-compliance by extracting and analyzing public platform data. Your solution will utilize Claude Opus 4.1 for its advanced capabilities in nuanced text analysis, legal interpretation, and ethical reasoning, ensuring accurate assessment against DSA mandates. OpenAI o3 will serve as a high-throughput data extraction agent. The entire system will be orchestrated using a custom agent architecture focusing on auditability and transparency, with secure inter-agent communication facilitated by the A2A Protocol. The agents will perform RAG over DSA legal texts and external data sources to build a comprehensive compliance report, highlighting potential violations and justifying their conclusions.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an A2A multi-agent system using Claude Opus 4.1 and OpenAI o3 for DSA compliance monitoring and public data access assessment.
How work is evaluated
The evaluation will assess the system's ability to accurately identify potential DSA compliance issues from simulated platform data, leveraging advanced RAG and ethical reasoning to generate a justified compliance report.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Architect an A2A multi-agent system for DSA compliance monitoring.
Integrate Claude Opus 4.1 for sophisticated legal text analysis and ethical reasoning.
Generate structured compliance reports detailing potential violations and evidence.
Implement advanced RAG over DSA legal documents and official guidance.
Employ OpenAI o3 for high-throughput public data extraction from simulated platforms.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The evaluation will assess the system's ability to accurately identify potential DSA compliance issues from simulated platform data, leveraging advanced RAG and ethical reasoning to generate a justified compliance rep...
Challenge input
{ 'dsa_articles_to_monitor': ['string'], 'mock_platform_data': [{ 'post_id': 'string', 'author': 'string', 'content': 'string', 'timestamp': 'strin...
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{ 'compliance_report': { 'platform_id': 'string', 'compliance_status': 'compliant|non-compliant|partial', 'violations_found': [{ 'article_id': 'str...
- Checks if the system correctly identifies the main compliance violation based on the provided input.
- Verifies that the identified violations are supported by correctly cited evidence IDs from the mock platfor...
- Ensures agent communication log entries adhere to the A2A protocol structure and content requirements.
- LegalReasoningQuality target: 0.88
- 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.
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Configured tools
No tool records are attached.
Evaluation contract
- Checks if the system correctly identifies the main compliance violation based on the provided input.
- Verifies that the identified violations are supported by correctly cited evidence IDs from the mock platform data.
- Ensures agent communication log entries adhere to the A2A protocol structure and content requirements.
Metrics
- LegalReasoningQuality · Target 0.88
- ReportCompleteness · Target 4
Workflow tasks
- Task 1
Input: { 'dsa_articles_to_monitor': ['string'], 'mock_platform_data': [{ 'post_id': 'string', 'author': 'string', 'content': 'string', 'timestamp': 'string', 'visibility': 'public|private', 'research_access_policy': 'string' }], 'mock_dsa_legal_text': 'string' }
Output: { 'compliance_report': { 'platform_id': 'string', 'compliance_status': 'compliant|non-compliant|partial', 'violations_found': [{ 'article_id': 'string', 'description': 'string', 'evidence_ids': ['string'], 'justification': 'string' }], 'recommendations': ['string'] }, 'agent_communication_log': [{ 'sender': 'string', 'receiver': 'string', 'protocol': 'A2A', 'message': 'string', 'timestamp': 'string' }] }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.