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Intelligent Mission Agent with Falcon 180B & Semantic Kernel
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Linked challenge: AI-Driven Space Logistics & Constellation Deployment Optimization with Falcon 180B
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Linked challenge
AI-Driven Space Logistics & Constellation Deployment Optimization with Falcon 180B
Prompt source
Original prompt text with formatting preserved for inspection.
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Develop an AI agent using Semantic Kernel to integrate Falcon 180B. This agent should be capable of: 1) interpreting high-level mission goals (e.g., 'deploy 6 satellites into a 600km polar orbit within 90 days with minimal fuel'), 2) invoking your orbital optimizer, 3) analyzing the generated mission plan's trade-offs (fuel vs. time), and 4) suggesting alternative strategies or refined constraints. Focus on effective prompt engineering and creating Semantic Functions to bridge the LLM with your simulation and optimization tools. The agent should be able to explain its reasoning.
Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.