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.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an autonomous factory maintenance scheduling orchestrator using the OpenAI Agents SDK and Cognition Devin to optimize production schedules against machine health degradation.
How work is evaluated
Evaluates the OpenAI Agents SDK scheduler on multi-line manufacturing plant scenarios, measuring constraint violation rates, OEE optimization efficiency, and execution runtime.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
zero_overlap_constraint
Ensures no machine is scheduled for maintenance and production simultaneously
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
rul_safety_compliance
Verifies no degraded machine is assigned production beyond its Remaining Useful Life threshold
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
oee_score
Computed Overall Equipment Effectiveness based on schedule output • target: 0.85 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
planning_latency_seconds
Time taken by agent orchestrator to synthesize the schedule • target: 10 • range: 0-60
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Architect a multi-agent workflow using OpenAI Agents SDK for factory maintenance and scheduling
Use Cognition Devin to automate integration test setup, synthetic environment execution, and code generation
Implement constraint-based optimization tools for balancing machine health deterioration against order fulfillment deadlines
Evaluate multi-agent trade-off negotiation and dynamic handoffs under operational plant bottlenecks
Reference links and supporting material
Factory operational logs from 5 discrete manufacturing facilities, comprising 500 machine health records, 1,200 work order execution logs, and historical maintenance downtime events under variable demand spikes.
Challenge setup
Evaluates the OpenAI Agents SDK scheduler on multi-line manufacturing plant scenarios, measuring constraint violation rates, OEE optimization efficiency, and execution runtime.
Challenge input
JSON object containing machine_statuses (array with RUL percentages), work_orders (array of order priority and deadlines), and maintenance_crew_shi...
OpenAI Agents SDK
Official OpenAI framework for orchestrating autonomous multi-agent function calling and handoffs
Cognition Devin
Enables automated code synthesis, test execution, and end-to-end integration harness validation
Evaluated output
JSON object with updated_production_schedule (array), scheduled_maintenance_blocks (array), and expected_plant_oee (float)
- Ensures no machine is scheduled for maintenance and production simultaneously
- Verifies no degraded machine is assigned production beyond its Remaining Useful Life threshold
- Computed Overall Equipment Effectiveness based on schedule output • target: 0.85 • range: 0-1
- Benchmark: Flexible Job-Shop Scheduling Benchmark (FJSP-Bench)
- Oee Score target: 0.85
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- OpenAI · Required
- Azure OpenAI Service · Optional
- OpenAI o3 · Optional
Evaluation contract
- zero_overlap_constraint · Weight 1
- rul_safety_compliance · Weight 1
- oee_score · Weight 1
- planning_latency_seconds · Weight 1
Metrics
- oee_score · Target 0.85
- planning_latency_seconds · Target 10
Benchmarks
- Flexible Job-Shop Scheduling Benchmark (FJSP-Bench)
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object containing machine_statuses (array with RUL percentages), work_orders (array of order priority and deadlines), and maintenance_crew_shifts (array)
Output: JSON object with updated_production_schedule (array), scheduled_maintenance_blocks (array), and expected_plant_oee (float)
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent Framework
- Cognition Devin · Autonomous Developer Agent System
Recipe freeze marker
No recipe freeze time is recorded.