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.
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.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Build A2A (Agent-to-Agent) protocol multi-agent systems for secure, auditable, and cross-platform communication between specialized agents.
Master the application of Claude Opus 4.1 for nuanced interpretation of complex legal texts (e.g., DSA articles), identifying subtleties in compliance requirements.
Deploy OpenAI o3 for efficient and scalable web scraping and public data extraction from various online platforms, handling rate limits and diverse data formats.
Design advanced RAG pipelines that prioritize context and legal relevance, combining vector search with semantic chunking of DSA regulations and related case law.
Implement extended thinking models with iterative refinement, allowing agents to re-evaluate their conclusions based on new evidence or legal interpretations.
Develop robust ethical AI safeguards, including bias detection in data extraction and a transparency layer for agent decision-making in compliance assessments.
Integrate tools for data sanitization and anonymization to handle public data responsibly and ensure privacy-preserving analysis.
Orchestrate a custom, auditable agent workflow for collecting evidence, cross-referencing against regulations, and generating structured compliance reports with justifications.
How this agent runs
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
The configured agent processes the input under the challenge policy.
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
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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