Intelligent Edge Operations
Design and implement a cutting-edge multi-agent system to automate and optimize network operations (NOC) within a simulated retail or factory environment. This challenge focuses on building intelligent agents that can monitor real-time sensor data, interact with enterprise systems (e.g., inventory, CRM, facility management), and trigger automated actions at the edge. The solution must demonstrate robust tool integration, adaptive reasoning capabilities for resource-constrained environments, and graph-based workflow orchestration. Participants will leverage the Model Context Protocol (MCP) for seamless and secure tool invocation, allowing agents to interact with diverse simulated edge devices and enterprise APIs. Agentkit will be essential for orchestrating complex, stateful agent workflows, enabling the system to handle unexpected events, perform predictive maintenance, and optimize operational efficiency in real-time. This challenge emphasizes practical, real-world application of advanced agentic AI in an industrial or commercial setting.
AI Research & Mentorship
What you are building
The core problem, expected build, and operating context for this challenge.
Design and implement a cutting-edge multi-agent system to automate and optimize network operations (NOC) within a simulated retail or factory environment. This challenge focuses on building intelligent agents that can monitor real-time sensor data, interact with enterprise systems (e.g., inventory, CRM, facility management), and trigger automated actions at the edge. The solution must demonstrate robust tool integration, adaptive reasoning capabilities for resource-constrained environments, and graph-based workflow orchestration. Participants will leverage the Model Context Protocol (MCP) for seamless and secure tool invocation, allowing agents to interact with diverse simulated edge devices and enterprise APIs. Agentkit will be essential for orchestrating complex, stateful agent workflows, enabling the system to handle unexpected events, perform predictive maintenance, and optimize operational efficiency in real-time. This challenge emphasizes practical, real-world application of advanced agentic AI in an industrial or commercial setting.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master Agentkit for building stateful agent workflows, including persistence and conditional routing.
Implement MCP (Model Context Protocol) for secure, standardized tool integration, enabling agents to interact with simulated APIs for inventory management, sensor data, and facility controls.
Deploy GPT 5 as a high-level reasoning and planning agent, leveraging its advanced capabilities for complex decision-making and problem-solving.
Integrate Gemini 2.5 Pro for multimodal perception tasks at the edge, such as analyzing camera feeds for anomaly detection or product recognition.
Design and implement hybrid instant/deep reasoning systems, allowing agents to quickly respond to common events and engage in deeper, more resource-intensive analysis for critical incidents.
Build adaptive thinking budgets into agent designs, dynamically adjusting reasoning depth and LLM calls based on system load, priority, and available edge compute resources.
Participation status
You haven't started this challenge yet
Operating window
Key dates and the organization behind this challenge.
Find another challenge
Jump to a random challenge when you want a fresh benchmark or a different problem space.