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.

• Master the AutoGen framework for creating ConversableAgents that represent distinct military assets.

• Implement tool-calling with Qwen 2.5 to interface with a Python-based tactical flight simulator (e.g., PyFly).

• Design prompt engineering strategies for 'Chain of Command' reasoning to enforce hierarchical decision-making.

• Build a state-management system to track swarm health, fuel, and munitions across the agent network.

• Orchestrate group chats in AutoGen to simulate radio-silence or jammed-communication environments using probability-based packet loss.

• Deploy a validation agent that checks proposed flight paths against G-force limits and energy management constraints.

• Optimize agent response latency for real-time tactical decision-making using model quantization and KV-caching.