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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design the architecture for your multi-agent system using CrewAI. Define the roles, goals, and tools for at least four agents (e.g., Procurement Agent, Logistics Agent, Risk Agent, Compliance Agent). Outline the schema for storing and retrieving market data, green certifications, and regulatory documents in Qdrant. How will Falcon 180B be integrated into the agents' decision-making processes?
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 role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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 Green Critical Mineral Supply Chain Optimization with Agentic Workflows
The global push for decarbonization is intensifying the demand for 'green' critical minerals, leading to complex supply chain challenges. From the "green iron" disputes to the volatile pricing of essential commodities like Codelco's copper, and massive infrastructure upgrades like EPA's lead pipe removal, managing these supply chains requires navigating market volatility, geopolitical risks, stringent sustainability standards, and evolving regulations. Traditional supply chain management often struggles with the dynamic nature of these factors. This challenge involves designing and implementing an advanced AI-powered system to optimize the end-to-end supply chain for a hypothetical 'green' critical mineral (e.g., green nickel for EV batteries). The system will leverage a multi-agent framework to simulate and manage procurement, logistics, and risk assessment, focusing on maintaining green certifications and adapting to real-time market changes. This requires integrating large language models with vector databases for knowledge retrieval and intelligent agent orchestration.
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.