A2A Ethical Generative Media Monitoring with GPT-5 & Claude Opus
Inspired by the backlash against AI-generated likenesses and calls for blocking mechanisms, this challenge focuses on building a sophisticated multi-agent system. You will design and implement an autonomous system capable of monitoring generative media content for potential ethical violations, policy breaches (e.g., unauthorized use of likeness), and managing requests to block specific digital representations. The system will leverage cutting-edge LLMs for advanced content analysis and ethical reasoning, orchestrated through a graph-based workflow with secure agent-to-agent communication. This project emphasizes modern AI approaches, including extended thinking patterns, multi-modal analysis, and a custom A2A protocol for seamless and secure information exchange between specialized agents. Success requires not only technical prowess in agentic AI but also a keen understanding of ethical AI principles and robust system design for real-world application in content moderation and digital rights management.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a graph-based multi-agent system using LangGraph and a custom A2A protocol with GPT-5 Pro and Claude Opus 4.1 for ethical generative media monitoring.
How work is evaluated
The evaluation will assess the system's accuracy in detecting policy violations, its efficiency in processing likeness blocking requests, and the robustness of its A2A communication and audit trail. Automated tests will simulate various scenarios.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Develop and utilize a secure A2A protocol for inter-agent communication and data exchange.
Implement mechanisms for detecting policy violations and handling likeness blocking requests.
Integrate multi-modal LLMs (GPT-5 Pro, Claude Opus 4.1) for advanced content and policy analysis.
Design and implement a multi-agent system using LangGraph for ethical content monitoring.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will assess the system's accuracy in detecting policy violations, its efficiency in processing likeness blocking requests, and the robustness of its A2A communication and audit trail. Automated tests wi...
Challenge input
{ 'media_url': 'string', 'policy_doc': 'string', 'context': 'string' }
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{ 'violation_detected': 'boolean', 'violation_type': 'string', 'confidence': 'float', 'reasoning': 'string', 'agents_involved': ['string'] }
- Verify that the system correctly identifies policy violations based on provided inputs and policies.
- Ensure likeness blocking requests are fully processed, leading to updated internal policies/lists.
- Check that all inter-agent communications adhered to the defined A2A protocol and were logged.
- DetectionAccuracy target: 0.9
- 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
- Verify that the system correctly identifies policy violations based on provided inputs and policies.
- Ensure likeness blocking requests are fully processed, leading to updated internal policies/lists.
- Check that all inter-agent communications adhered to the defined A2A protocol and were logged.
- Assess if the 'reasoning' field provides a coherent and ethically sound explanation for decisions.
Metrics
- DetectionAccuracy · Target 0.9
- ProcessingLatency · Target 5
- FalsePositiveRate · Target 0.02
Workflow tasks
- Task 1
Input: { 'media_url': 'string', 'policy_doc': 'string', 'context': 'string' }
Output: { 'violation_detected': 'boolean', 'violation_type': 'string', 'confidence': 'float', 'reasoning': 'string', 'agents_involved': ['string'] }
Execution harness: Python
- Task 2
Input: { 'request_id': 'string', 'public_figure_name': 'string', 'requester_info': 'string', 'action_type': 'string' }
Output: { 'request_status': 'string', 'processing_log': ['string'], 'blocked_entities': ['string'], 'confirmation_message': 'string' }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.