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.
Using LangChain and LangGraph, define a state machine for an SDA monitoring system. Create nodes for 'Ingest_TLE', 'Physics_Check', and 'Report_Generator'. The state should store the current orbital elements and a history of detected anomalies. Use LastMile AI's model via ChatOpenAI-compatible wrappers to perform the reasoning in the 'Physics_Check' node.
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.
Autonomous Space Domain Awareness (SDA) Agent with LangGraph and LastMile AI
Inspired by the Space Force's 'RG-XX neighborhood watch' satellites and the call for a 'Planetary Neural Network,' this challenge tasks you with building a multi-agent system for autonomous orbital monitoring. Using LangChain and LangGraph, you will orchestrate a workflow where specific agents ingest Two-Line Element (TLE) sets, simulate orbital trajectories using SGP4, and utilize LLMs to identify non-cooperative maneuvers or potential conjunction events. The system must process disparate telemetry sources and generate actionable 'Space Situational Awareness' reports for satellite operators. You will integrate LastMile AI models to perform high-reasoning tasks on orbital state vectors, identifying anomalies that deviate from predicted Keplerian physics. The architecture should leverage stateful workflows to track satellite 'life cycles' and maintain a persistent memory of historical maneuver patterns to distinguish between routine station-keeping and adversarial proximity operations.
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.