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Mastra AI Project Setup and Agent Definition

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Linked challenge: High-Performance Operational Planning Agent

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Linked challenge

High-Performance Operational Planning Agent

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Initialize a new Mastra AI project in TypeScript. Define a 'FleetManager' agent with a specific goal of optimizing autonomous vehicle dispatch. Set up basic memory for the agent to track vehicle states. Implement a placeholder tool `get_fleet_status()`.

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Linked challenge

High-Performance Operational Planning Agent

Design and implement a high-performance, real-time operational planning agent using the Mastra AI TypeScript framework. This challenge focuses on creating a robust system capable of handling complex logistics scenarios, such as autonomous fleet dispatch or dynamic resource allocation, inspired by systems like Waymo's next-gen robotaxis. The agent will leverage the advanced reasoning capabilities of GPT-5 for strategic planning and decision-making, complemented by Claude Sonnet 4 for critical validation and safety checks, ensuring reliable operations under uncertainty. Both models will be served efficiently via the Cohere Platform, providing a scalable and secure inference environment. To achieve ultra-low-latency responses crucial for real-time operations, NVIDIA's TensorRT-LLM will be employed to optimize and accelerate the model inference. Mastra AI's built-in memory management and tool-use capabilities will be central to the agent's ability to maintain situational awareness and interact with various operational systems. This challenge highlights the integration of high-performance inference technologies with advanced agent frameworks for critical, real-world applications.

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