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.
Build an advanced multi-agent system using LangGraph and GPT-5 for autonomous drone swarm mission orchestration, featuring A2A communication and MCP-enabled tool integration.
How work is evaluated
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 robustness.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Integrate GPT-5 for high-level strategic reasoning with extended thinking and adaptive budgets.
Implement the A2A Protocol for secure, real-time agent-to-agent communication within the swarm.
Design and implement graph-based agent workflows using LangGraph for dynamic mission planning.
Orchestrate a multi-agent swarm capable of dynamic task allocation and adaptive mission execution.
Develop MCP-enabled tool agents to interface with simulated drone control and sensor systems.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
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
No agent tool is listed for this challenge.
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
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
No tool records are attached.
Evaluation contract
- 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 telemetry).
Metrics
- Mission Completion Rate · Target 90
- Adaptation Time · Target 30
- Resource Optimization · Target 0.8
Workflow tasks
- Task 1
Input: JSON object with 'mission_objectives': string[], 'area_constraints': string[], 'drone_capabilities': string[]
Output: JSON object with 'initial_plan': { 'steps': string[], 'duration_estimate': string, 'resource_allocation': object }, 'reasoning_trace': string
Execution harness: Python
- Task 2
Input: JSON object with 'current_state': object, 'new_event': { 'type': string, 'details': string }
Output: JSON object with 'revised_plan': { 'steps': string[], 'reasoning': string }, 'a2a_log_excerpt': string
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.