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.
Develop the core logic for the 'Mission Planner' and 'Resource Manager' agents. Explain how they will dynamically assign tasks to satellites, optimize power usage for K2 Space's high-power satellites, and manage data buffers to prevent overload. How will Reinforcement Learning be applied here, and what reward functions would you use? This directly relates to the 'ResourceOptimization' evaluation task.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
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 Satellite Tasking & Anomaly Response with Llama 3.3 and CrewAI
This challenge focuses on building an intelligent agent capable of autonomously managing a constellation of high-power satellites, responding dynamically to mission objectives, resource constraints, and detected anomalies. Inspired by BAE Systems' DARPA award for autonomous satellite tasking, Voyager's AI-driven signals processing, and K2 Space's high-power satellite development, the core problem addresses the increasing complexity and data overload faced by human operators in modern space operations. Participants will design a multi-agent AI system that can prioritize tasks, optimize power distribution, and initiate defensive maneuvers or data collection in response to unexpected events, such as potential jamming attempts or system malfunctions. The system must leverage large language models for interpreting high-level mission directives and context, and a multi-agent framework for orchestrating complex decision-making processes. The solution should demonstrate robustness in handling dynamic environments and optimizing operational efficiency, simulating a real-world scenario where satellite assets must adapt quickly to maintain mission integrity and effectiveness against various threats or opportunities. The challenge emphasizes practical implementation of AI for enhancing space defense capabilities and managing advanced satellite technologies.
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.