New Tool Generates Synthetic Scenarios for Industry 4.0 Agent Evaluation

Evaluating AI agents in Industry 4.0 settings requires realistic and comprehensive scenarios that incorporate complex data such as telemetry, failure modes, and maintenance records, all while adhering to domain standards. Current benchmarks often rely on manually created scenarios, which are limited in scope and asset classes. This new research extends the AssetOpsBench benchmark by adding a Smart Grid Transformer asset class and four IEC-grounded diagnostic tools. The core innovation is the ScenarioGeneratorAgent pipeline, designed for synthetic industrial-agent scenario generation. This pipeline systematically constructs evidence-grounded asset profiles, intelligently allocates scenario budgets across operational domains, and generates candidates through a hybrid validation-and-repair loop. This loop ensures schema validity, tool reachability, physical plausibility, standards alignment, and deduplication, guaranteeing high-quality scenarios. To enhance scalability, the pipeline incorporates several optimizations, including two-level caching, parallel focus-group generation, thread-pool offloading, batched LLM calls, and early rejection filtering. These improvements reduce runtime by eight times for 50 scenarios while maintaining scenario quality, demonstrating an efficient method to expand industrial-agent benchmarks without compromising realism.