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 a LangGraph agent that leverages Llama 3.1 405B. This agent should receive the current battle space state (threat positions, velocities, priorities; interceptor states, fuel levels) and, based on mission goals, output strategic directives such as target prioritization schemes, engagement zones, or high-level swarm behaviors (e.g., 'defensive formation', 'aggressive pursuit'). Provide example prompts you would use for Llama 3.1 and how its responses would be parsed by LangGraph to inform tactical algorithms.
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 Space-Based Interceptor Swarm Orchestration
The rapid proliferation of space assets and emerging threats necessitates advanced, autonomous defense capabilities. This challenge focuses on developing an intelligent control system for a swarm of space-based interceptors. Participants will design and implement a multi-agent system capable of real-time threat assessment, dynamic target assignment, and optimal trajectory planning in a complex, contested orbital environment. Leveraging state-of-the-art Generative AI, specifically Llama 3.1 405B, integrated via LangGraph, the system must adapt its strategic decisions based on evolving threat landscapes and resource constraints. The goal is to maximize the neutralization of incoming threats while minimizing interceptor usage and ensuring robust operation under uncertainty, mimicking real-world space defense scenarios.
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.