Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Run your CrewAI system with the defined scenario and agents. Capture the final 'Negotiation Playbook' and 'Predicted Outcomes' generated by the collaborative agent team. Analyze the quality, consistency, and strategic depth of the output, comparing it against your initial expectations.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the rubric, target behavior, and pass-fail criteria as the baseline for evaluation.
Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.
Make sure the prompt catches regressions instead of just mirroring the happy-path examples.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
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.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.