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 AutoGen, define four distinct agents: a MissionCommander (UserProxy), a TacticalLead (Assistant), an EWWingman (Specialist), and a StrikeWingman (Specialist). Detail their system messages to reflect military hierarchy and specific technical roles.
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.
Orchestrating Autonomous CCA Swarms
This challenge requires developers to build a decentralized multi-agent system for drone wingmen. You will design a hierarchy of autonomous agents capable of performing mission planning, sensor fusion, and tactical execution in a simulated contested environment. The core focus is on task decomposition: how a lead 'human-in-the-loop' agent delegates high-risk roles (e.g., electronic warfare, decoy, or kinetic strike) to autonomous wingmen while maintaining strict adherence to Rules of Engagement (ROE). Participants will utilize the AutoGen framework to manage agent conversations and the Qwen 2.5-72B model for tactical reasoning. The simulation must handle 'dynamic re-tasking'—where an agent must pivot its objective if a peer is neutralized or a new high-priority threat (like the Russian Oreshnik missile system) is detected. Success is measured by the swarm's ability to minimize attrition while achieving primary mission objectives within a defined physics-based simulation window.
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.