Automated Work-Order & Preventive Scheduling Agent with OpenAI Agents SDK & n8n
Predictive maintenance only delivers true ROI when anomalous diagnostic insights seamlessly trigger maintenance scheduling and material provisioning across plant Enterprise Resource Planning (ERP) systems. In this challenge, participants will use the OpenAI Agents SDK integrated with n8n workflow automation to build an end-to-end predictive dispatch pipeline. The system receives equipment health alerts, invokes OpenAI Agents SDK tools to parse maintenance histories, determines spare parts availability, and triggers an n8n webhook workflow to automatically schedule technician dispatch and order replacement components in SAP/Maximo simulated environments.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an automated predictive maintenance dispatch agent using OpenAI Agents SDK and n8n to generate work orders and order spare parts.
How work is evaluated
Evaluates whether the agent correctly triggers the n8n webhook and creates valid work orders with accurate spare part allocations.
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.
Priority Allocation Test
High urgency alerts (< 8 hours TTD) must produce HIGH priority work orders
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Parts Match Accuracy
Accuracy of required spare parts selection based on failure mode • target: 1 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement an autonomous agent using OpenAI Agents SDK with custom tool definitions
Connect agent reasoning steps to n8n webhook automation flows
Generate structured work-order payloads with labor and parts estimates
Optimize technician dispatch based on priority and machine downtime cost
Reference links and supporting material
Synthetic maintenance failure logs and corresponding bill-of-materials (BOM) catalog for industrial CNC mills and conveyor systems.
Challenge setup
Evaluates whether the agent correctly triggers the n8n webhook and creates valid work orders with accurate spare part allocations.
Challenge input
JSON fault alert (machine_id, failure_mode, estimated_ttd_hours)
OpenAI Agents SDK
Assigned primary agent SDK
n8n
Assigned workflow integration tool
Evaluated output
JSON work-order payload (order_id, assigned_tech_role, parts_required, priority)
- High urgency alerts (< 8 hours TTD) must produce HIGH priority work orders
- Accuracy of required spare parts selection based on failure mode • target: 1 • range: 0-1
- Benchmark: Enterprise Agent Workflow Benchmark
- Parts Match Accuracy target: 1
- 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.
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Configured tools
- OpenAI · Required
- Azure OpenAI Service · Optional
- OpenAI o3 · Optional
Evaluation contract
- Priority Allocation Test · Weight 1
- Parts Match Accuracy · Weight 1
Metrics
- Parts Match Accuracy · Target 1
Benchmarks
- Enterprise Agent Workflow Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON fault alert (machine_id, failure_mode, estimated_ttd_hours)
Output: JSON work-order payload (order_id, assigned_tech_role, parts_required, priority)
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent orchestration
- n8n · Workflow automation platform
Recipe freeze marker
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