AI Content Licensing Agent
To establish rules for AI web crawlers, this challenge tasks you with building an advanced A2A multi-agent system using LangGraph and Marvin to automate content licensing negotiations between 'Publisher Agents' and 'Crawler Agents.' Each agent type will be powered by Claude Opus 4.5, leveraging its sophisticated reasoning for contract understanding and negotiation. The system must implement graph-based workflows to manage negotiation states, enforce content rules (simulated), and ensure secure, auditable transactions via an MCP for contract finalization and compensation. This involves designing dynamic pricing models, handling counter-proposals, and ensuring compliance through agent-to-agent communication.
What you are building
The core problem, expected build, and operating context for this challenge.
To establish rules for AI web crawlers, this challenge tasks you with building an advanced A2A multi-agent system using LangGraph and Marvin to automate content licensing negotiations between 'Publisher Agents' and 'Crawler Agents.' Each agent type will be powered by Claude Opus 4.5, leveraging its sophisticated reasoning for contract understanding and negotiation. The system must implement graph-based workflows to manage negotiation states, enforce content rules (simulated), and ensure secure, auditable transactions via an MCP for contract finalization and compensation. This involves designing dynamic pricing models, handling counter-proposals, and ensuring compliance through agent-to-agent communication.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for building complex, stateful Directed Acyclic Graph (DAG) workflows for multi-agent interactions.
Implement the A2A (Agent-to-Agent) protocol for secure, authenticated, and verifiable communication between distinct agent roles.
Design and create structured agent personas using Marvin, leveraging its declarative syntax for agent capabilities and output schemas.
Utilize Claude Opus 4.5 for its advanced natural language understanding, logical reasoning, and ability to process long-context legal-like documents for contract negotiation.
Develop extended thinking capabilities for agents to analyze counter-proposals, evaluate risks, and adapt negotiation tactics.
Build an MCP service for managing contract states, logging negotiation history, and integrating with simulated payment/blockchain systems for compensation.
Orchestrate a multi-agent simulation where Publisher Agents dynamically price content based on RSL 1.0 rules and Crawler Agents negotiate for access.
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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