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OpenAI Agents SDK Setup for Factory Scheduling

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Linked challenge: Autonomous Factory Maintenance Scheduler with OpenAI Agents SDK and Cognition Devin

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Linked challenge

Autonomous Factory Maintenance Scheduler with OpenAI Agents SDK and Cognition Devin

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Using Python and the OpenAI Agents SDK (`from agents import Agent, Runner, function_tool`), create a multi-agent environment with three specialized agents: `HealthDiagnosticsAgent`, `ProductionPlannerAgent`, and `MaintenanceSchedulerAgent`. Implement tool definitions for querying machine RUL and solver constraint functions.

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Linked challenge

Autonomous Factory Maintenance Scheduler with OpenAI Agents SDK and Cognition Devin

Industrial facilities struggling to scale Industry 4.0 initiatives often experience bottlenecks in dynamically scheduling preventative maintenance alongside active production orders. When vibration anomalies or thermal degradations are detected, plant managers must manually calculate trade-offs between machine downtime, tooling availability, workforce shift schedules, and batch fulfillment deadlines. Static maintenance calendars result in either catastrophic unscheduled equipment failure or costly unnecessary maintenance interventions. In this challenge, you will build an autonomous multi-agent factory scheduling orchestrator using the OpenAI Agents SDK integrated with automated dev and execution workflows managed via Cognition Devin. The agent system will continuously evaluate machine health indices, maintenance work orders, labor shifts, and production constraints to generate optimized, conflict-free plant maintenance schedules. Using the OpenAI Agents SDK's multi-turn conversational agents, tool calling, and handoff mechanisms alongside Cognition Devin's autonomous environment debugging capabilities, your solution will construct dynamic job schedules that maximize plant OEE (Overall Equipment Effectiveness) while guaranteeing safety and maintenance compliance.

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