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

2026-01-01 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cloud Computing & IT Infrastructure · Depth: Advanced, extended

Summary

DynAMO (Dynamic Asset Management Orchestration) is a deployment-ready engine designed to automate industrial asset lifecycles using LLM-powered agents, addressing latency, concurrency instability, and safety risks in Industry 4.0. It employs a Plan-then-Execute architecture to generate verifiable workflow graphs, supporting both sequential and dependency-aware parallel execution. Across six experiments on AssetOpsBench, DynAMO achieved a median 1.6x latency reduction (up to 1.8x on highly parallelizable workflows) through parallel execution. It demonstrated that LLM inference and orchestration account for over 90% of execution time, even with realistic tool I/O latencies. Structured context pruning further reduced inference latency by approximately 30%, while maintaining functional correctness and exhibiting graceful degradation under fault injection. The system also confirmed stable execution and reduced latency variance under repeated runs.

Key takeaway

For MLOps Engineers deploying LLM-powered agents in Industry 4.0, DynAMO's approach highlights critical design considerations. You should prioritize schema-constrained planning and dependency-aware parallel execution to achieve median 1.6x latency reductions and enhance system stability. Focus optimization efforts on LLM inference and context efficiency, as these dominate over 90% of execution time, rather than micro-optimizing orchestration. Implement structured context pruning to reduce inference latency by approximately 30%.

Key insights

DynAMO orchestrates LLM agents for industrial automation, using schema-validated planning and dynamic parallel execution to reduce latency and ensure safety.

Principles

• Validate task graphs pre-execution.
• Dynamically parallelize independent tasks.
• Prioritize inference/context efficiency.

Method

DynAMO uses a Plan-then-Execute architecture. It generates schema-constrained, verifiable workflow DAGs, then executes them with a topological engine that dynamically dispatches tasks to a thread pool, leveraging agent-tool metadata for resource-aware scheduling.

In practice

• Use schema-constrained planning for correctness.
• Implement token-budgeted context pruning.
• Isolate runtime failures for fault tolerance.

Topics

• LLM Agents
• Industrial Automation
• Workflow Orchestration
• Parallel Execution
• Latency Optimization
• AssetOpsBench

Code references

• kushwaha001/DynAMO

Best for: AI Architect, AI Scientist, Research Scientist, MLOps Engineer, AI Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.