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Implement Logistics Agent and Dynamic Pathfinding

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Linked challenge: Multi-Agent Warehouse Optimization

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Linked challenge
Multi-Agent Warehouse Optimization

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Implement the 'Logistics Agent' using Mastra AI. This agent should receive tasks (e.g., 'move item SKU to location Y', 'pick up SKU X') and use a simulated 'pathfinding' tool to generate optimal routes. When new high-priority tasks or obstacles are reported (simulated events), the agent should dynamically re-evaluate and optimize its current path using Claude Sonnet 4's reasoning. Ensure it can communicate changes to other relevant agents or the human supervisor via Coplay AI.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.