DynAMO:Dynamic Asset Management Orchestration via Topological Multi-Agent Scheduling

DynAMO is a deployment-ready orchestration engine for LLM-powered agents that solves latency and safety challenges in industrial automation through a Plan-then-Execute architecture supporting both sequential and parallel task execution. Benchmarks show 1.6-1.8x latency reduction via parallelization while maintaining safety and functional correctness, positioning the technology as practical infrastructure for Industry 4.0 automation at scale.

DynAMO addresses a critical gap between theoretical LLM agent capabilities and real-world industrial deployment constraints. While language models have demonstrated strong reasoning abilities for automation tasks, production systems require guarantees around latency, concurrency stability, and safety—requirements that existing agent frameworks struggle to meet. The research identifies that LLM inference represents over 90% of execution time, making orchestration optimization the primary lever for performance gains.

The technical contribution lies in generating verifiable workflow graphs that intelligently decompose tasks into parallelizable units without sacrificing correctness. By dynamically identifying independent operations, DynAMO enables concurrent execution while maintaining topological ordering guarantees that prevent task sequencing errors. This represents a meaningful advance beyond naive parallelization approaches, which risk breaking dependencies and introducing safety vulnerabilities.

For the industrial automation sector, this work establishes practical patterns for scaling agent-based systems beyond proof-of-concept. The 30% inference latency reduction through context pruning and graceful degradation under fault injection demonstrate engineering discipline that enterprises require before deploying LLM systems in production environments. The reproducibility analysis confirming stable execution under repeated runs addresses operational concerns around system predictability.

The benchmarking methodology using the AssetOpsBench industrial dataset provides evidence that generalizes beyond toy problems. However, the work's impact depends on adoption by orchestration frameworks and enterprise platforms. The released code enables community validation and integration into existing automation pipelines. Future directions include optimizing LLM inference itself rather than just orchestration, potentially through specialized quantization or distillation approaches targeting the identified bottleneck.

• →DynAMO achieves 1.6-1.8x latency reduction through parallelizable task scheduling while maintaining safety and correctness guarantees
• →LLM inference comprises over 90% of execution time, indicating orchestration optimization provides diminishing returns versus model-level improvements
• →Structured context pruning reduces inference latency by approximately 30% without compromising functional behavior or output quality
• →Graceful degradation and stable reproducibility across repeated runs demonstrate production-readiness for enterprise deployment scenarios
• →The Plan-then-Execute architecture generating verifiable workflow graphs provides a practical blueprint for safe agent orchestration in Industry 4.0