Multi-Agent Warehouse Optimization
This challenge tasks developers with creating a multi-agent system for autonomous warehouse monitoring and optimization, inspired by 'curious AI' for drones. Using Mastra AI, you will design a team of agents that simulate real-time observation, inventory management, and logistical pathfinding. The system will integrate real-time (simulated) data from 'curious' camera/drone agents, allowing for dynamic adjustments to inventory placement and pick-up routes. Mastra AI's built-in memory and tool-use capabilities will be crucial for agents to maintain a consistent understanding of the warehouse state and interact with simulated external systems (like inventory databases or drone control APIs). Claude Sonnet 4 will power the agents' real-time decision-making and provide a human-readable interface through Coplay AI for supervisors to monitor and, if necessary, intervene in operations. BentoML Cloud will be used to deploy and serve simulated 'curious AI' inference modules, demonstrating scalable edge intelligence.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge tasks developers with creating a multi-agent system for autonomous warehouse monitoring and optimization, inspired by 'curious AI' for drones. Using Mastra AI, you will design a team of agents that simulate real-time observation, inventory management, and logistical pathfinding. The system will integrate real-time (simulated) data from 'curious' camera/drone agents, allowing for dynamic adjustments to inventory placement and pick-up routes. Mastra AI's built-in memory and tool-use capabilities will be crucial for agents to maintain a consistent understanding of the warehouse state and interact with simulated external systems (like inventory databases or drone control APIs). Claude Sonnet 4 will power the agents' real-time decision-making and provide a human-readable interface through Coplay AI for supervisors to monitor and, if necessary, intervene in operations. BentoML Cloud will be used to deploy and serve simulated 'curious AI' inference modules, demonstrating scalable edge intelligence.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.
Agent State Consistency
Mastra AI agents maintain consistent internal state and memory across interactions.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Tool Use Efficacy
Agents successfully use simulated tools to interact with the warehouse environment.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Resolution Time (seconds)
Average time taken by agents to resolve simulated discrepancies. • target: 90 • range: 30-300
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Path Optimization Ratio
Efficiency of optimized paths compared to baseline non-optimized paths (lower is better for cost/time). • target: 0.75 • range: 0.5-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master Mastra AI for building stateful, multi-agent systems, leveraging its built-in memory management and asynchronous agent communication patterns.
Design and implement tool-use capabilities within Mastra AI agents to interact with simulated warehouse management systems (e.g., inventory databases, drone control APIs).
Deploy and serve lightweight 'curious AI' inference models (e.g., object detection for inventory) using BentoML Cloud, demonstrating scalable edge intelligence for real-time data capture.
Integrate Claude Sonnet 4 as the reasoning engine for Mastra AI agents, enabling sophisticated decision-making for inventory optimization and dynamic pathfinding.
Build a real-time human-in-the-loop interface using Coplay AI, allowing human supervisors to monitor agent activities, receive alerts, and provide directives to the Mastra AI agents.
Implement dynamic task allocation and coordination strategies among Mastra AI agents to respond to real-time changes in warehouse conditions (e.g., new shipments, misplaced items).
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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