AutomatedContractNegotiation
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 strategies will be key.
Evaluation overview
How the linked challenge is judged: tasks, benchmarks, and criteria count.
Task templates
Inputs and expected outputs.
AutomatedContractNegotiation
Simulate multiple negotiation rounds between a Publisher Agent and a Crawler Agent based on a given content asset and RSL 1.0 rules. The goal is to reach a signed agreement or log a failed negotiation.
{'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}}
{'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': '...'}