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.
Develop an A2A multi-agent system using LangGraph and Marvin, powered by Claude Opus 4.5, to automate AI content licensing negotiations and enforce rules via MCP.
How work is evaluated
Evaluation will focus on the negotiation system's ability to successfully reach agreements, adhere to content access rules, and record transactions securely via MCP. The quality of agent decision-making and negotiation strategies will be key.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Implement a graph-based multi-agent negotiation workflow using LangGraph.
Build sophisticated 'Publisher' and 'Crawler' agents with Claude Opus 4.1 for negotiation and rule interpretation.
Develop adaptive negotiation strategies with extended thinking for optimal outcomes.
Integrate an MCP for secure contract exchange, enforcement, and compensation tracking.
Design and utilize the A2A protocol for secure and structured agent-to-agent communication.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
Evaluation will focus on the negotiation system's ability to successfully reach agreements, adhere to RSL 1.0 rules, and record transactions securely via MCP. The quality of agent decision-making and negotiation strat...
Challenge input
{'content_asset_id': 'abc123', 'publisher_rules': {'min_price_per_token': 0.001, 'licenses_available': ['commercial', 'research']}, 'crawler_intere...
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'negotiation_id': '...', 'status': 'success'|'failure', 'final_agreement': {'content_asset_id': '...', 'license_type': 'commercial', 'price_per_to...
- Verifies that all signed agreements adhere to the initial RSL 1.0 rules (e.g., min price, license types).
- Confirms that agents communicate securely and exclusively via the defined A2A protocol.
- Checks if the final agreement is successfully recorded in the simulated MCP ledger.
- NegotiationSuccessRate target: 80%
- 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
- Verifies that all signed agreements adhere to the initial RSL 1.0 rules (e.g., min price, license types).
- Confirms that agents communicate securely and exclusively via the defined A2A protocol.
- Checks if the final agreement is successfully recorded in the simulated MCP ledger.
Metrics
- NegotiationSuccessRate · Target 0.8
- AverageRoundsPerNegotiation · Target 3
Workflow tasks
- Task 1
Input: {'content_asset_id': 'abc123', 'publisher_rules': {'min_price_per_token': 0.001, 'licenses_available': ['commercial', 'research']}, 'crawler_interest': {'tokens_desired': 100000, 'license_type': 'commercial', 'max_bid_per_token': 0.0015}}
Output: {'negotiation_id': '...', 'status': 'success'|'failure', 'final_agreement': {'content_asset_id': '...', 'license_type': 'commercial', 'price_per_token': 0.0012, 'total_cost': 120.0, 'signed_by_publisher': true, 'signed_by_crawler': true}, 'negotiation_log': [...], 'mcp_transaction_id': '...'}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.