Autonomous Swarm Mission Orchestration
Develop a cutting-edge multi-agent system capable of orchestrating autonomous drone swarms for complex, dynamic missions. This challenge requires building agents that can plan, execute, and adapt mission strategies in real-time. The system must leverage advanced generative AI models for high-level reasoning and agentic frameworks for robust inter-agent communication and state management. Participants will integrate GPT-5 for strategic mission planning and decision-making, using extended thinking techniques with adaptive reasoning budgets to handle unforeseen circumstances. LangGraph will be essential for defining the stateful, graph-based workflows of individual and team agents, enabling dynamic re-planning. Crucially, the system will implement the A2A (Agent-to-Agent) Protocol for secure and efficient communication between swarm agents, ensuring coordinated action and fault tolerance. MCP will facilitate tool integration with simulated drone telemetry and control APIs.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a cutting-edge multi-agent system capable of orchestrating autonomous drone swarms for complex, dynamic missions. This challenge requires building agents that can plan, execute, and adapt mission strategies in real-time. The system must leverage advanced generative AI models for high-level reasoning and agentic frameworks for robust inter-agent communication and state management. Participants will integrate GPT-5 for strategic mission planning and decision-making, using extended thinking techniques with adaptive reasoning budgets to handle unforeseen circumstances. LangGraph will be essential for defining the stateful, graph-based workflows of individual and team agents, enabling dynamic re-planning. Crucially, the system will implement the A2A (Agent-to-Agent) Protocol for secure and efficient communication between swarm agents, ensuring coordinated action and fault tolerance. MCP will facilitate tool integration with simulated drone telemetry and control APIs.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for building complex, stateful agent workflows, including dynamic branching and persistence.
Implement A2A protocol for robust, secure, and authenticated agent-to-agent communication channels, focusing on message passing and state synchronization.
Design and deploy MCP-enabled tool agents to interact with external systems, specifically simulating drone telemetry, navigation, and payload control APIs.
Utilize GPT-5 (e.g., GPT-5 Pro or OpenAI o3 equivalent) for advanced strategic reasoning, employing extended thinking techniques and adaptive reasoning budgets for resource-constrained environments.
Build a hierarchical multi-agent system where a 'Commander' agent (GPT-5 powered) directs 'Squad' agents, which in turn coordinate 'Drone' agents via A2A protocol.
Develop adaptive planning algorithms that allow the swarm to re-evaluate and re-plan missions in response to unexpected events or environmental changes.
Integrate RAG (Retrieval Augmented Generation) to provide agents with real-time intelligence feeds (simulated satellite imagery, threat assessments) to inform decision-making.
Implement safety and redundancy measures within the agent system to handle agent failures or communication losses in a simulated environment.
How this agent runs
The evaluation will assess the system's ability to autonomously plan, execute, and adapt a drone swarm mission under simulated dynamic conditions. Emphasis will be on agent coordination, decision-making quality, and r...
Challenge input
JSON object with 'mission_objectives': string[], 'area_constraints': string[], 'drone_capabilities': string[]
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
JSON object with 'initial_plan': { 'steps': string[], 'duration_estimate': string, 'resource_allocation': object }, 'reasoning_trace': string
- Verify successful A2A message exchange between swarm agents during mission execution.
- Ensure MCP-enabled tools correctly simulate interaction with drone APIs (e.g., sending commands, receiving...
- Mission Completion Rate target: 90
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
DocsFind another challenge
Jump to a random challenge when you want a fresh benchmark or a different problem space.