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Challenge

AI-Driven Media Rights Negotiation

Design and implement an advanced agentic system to assist with complex media rights negotiations. This challenge focuses on building a multi-agent team that can analyze contracts, market data, and strategic objectives to propose optimal negotiation strategies and predict outcomes. The system will leverage Claude Opus 4.1 for its superior reasoning capabilities in understanding legal and business nuances, employing a hybrid reasoning approach for instant strategic insights and deeper analysis. CrewAI will be used to orchestrate a team of specialized agents—such as a Legal Analyst, Market Strategist, and Business Development Lead—each with distinct roles and access to RAG-powered knowledge bases (simulated contract databases, market reports). The agents will engage in internal 'extended thinking' dialogues to refine their positions and collaboratively generate negotiation playbooks, including adaptive thinking budgets for resource-intensive analysis.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a CrewAI multi-agent system powered by Claude Opus 4.1 for strategic media rights negotiation, using hybrid reasoning and RAG for data-driven insights.

Delivery guide

How work is evaluated

Evaluation

The system will be evaluated on its ability to generate a comprehensive and strategically sound negotiation playbook for a simulated media rights dispute, along with plausible outcome predictions and justifications.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Learning goals

What you should walk away with

  • Orchestrate a role-based multi-agent team using CrewAI for negotiation analysis.

  • Integrate Claude Opus 4.1 for advanced reasoning in legal and business contexts.

  • Generate a comprehensive negotiation playbook with predicted outcomes and alternative strategies.

  • Implement RAG systems to provide agents with up-to-date contract terms and market data.

  • Develop a hybrid instant/deep reasoning pipeline for dynamic strategic advice.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 2 tasks

Challenge setup

The system will be evaluated on its ability to generate a comprehensive and strategically sound negotiation playbook for a simulated media rights dispute, along with plausible outcome predictions and justifications.

Challenge input

JSON object with 'dispute_scenario': string, 'client_objectives': string[], 'simulated_contracts': object, 'market_data_summary': string

Agent execution

No agent tool is listed for this challenge.

Evaluated output

JSON object with 'negotiation_playbook': { 'strategies': string[], 'key_arguments': string[], 'concession_points': string[], 'red_lines': string[]...

Evaluation checks
  • Verify that all specified agents (Legal Analyst, Market Strategist, Business Development) actively contribu...
  • Ensure retrieved information from simulated RAG sources is accurately referenced and used by agents.
Configured evaluation evidence
  • Strategic Coverage target: 4
Configured runtime
  • 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

  • Verify that all specified agents (Legal Analyst, Market Strategist, Business Development) actively contributed to the final output.
  • Ensure retrieved information from simulated RAG sources is accurately referenced and used by agents.

Metrics

  • Strategic Coverage · Target 4
  • Prediction Accuracy (Plausibility) · Target 4
  • Risk Identification · Target 4

Workflow tasks

  1. Task 1

    Input: JSON object with 'dispute_scenario': string, 'client_objectives': string[], 'simulated_contracts': object, 'market_data_summary': string

    Output: JSON object with 'negotiation_playbook': { 'strategies': string[], 'key_arguments': string[], 'concession_points': string[], 'red_lines': string[] }, 'predicted_outcomes': string

    Execution harness: Python

  2. Task 2

    Input: JSON object with 'proposed_strategy': string, 'market_conditions': object

    Output: JSON object with 'risks': string[], 'mitigation_plan': string[]

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about AI-Driven Media Rights Negotiation