Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines
📰 ArXiv cs.AI
Learn to optimize agentic plan-execute pipelines using temporal semantic caching and workflow optimization to reduce latency in industrial asset operations workflows
Action Steps
- Evaluate the plan-execute pipeline using AssetOpsBench (AOB) to identify bottlenecks
- Apply temporal semantic caching to reduce overhead from tool discovery and LLM planning
- Optimize workflow execution using MCP tool execution and final summarization
- Compare the performance of different caching techniques and workflow optimizations
- Implement the optimized pipeline in a real-world industrial asset operations workflow
Who Needs to Know This
Data scientists and software engineers working on industrial asset operations workflows can benefit from this research to improve the efficiency of their pipelines
Key Insight
💡 Temporal semantic caching and workflow optimization can significantly reduce latency in agentic plan-execute pipelines
Share This
💡 Reduce latency in industrial asset operations workflows with temporal semantic caching and workflow optimization!
Key Takeaways
Learn to optimize agentic plan-execute pipelines using temporal semantic caching and workflow optimization to reduce latency in industrial asset operations workflows
Full Article
Title: Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines
Abstract:
arXiv:2605.20630v1 Announce Type: new Abstract: Industrial asset operations workflows are latency-sensitive because a single user query may require coordination over sensor data, work orders, failure modes, forecasting tools, and domain-specific agents. We evaluate this problem on AssetOpsBench (AOB), an industrial agent benchmark whose plan-execute pipeline exposes repeated overhead from tool discovery, LLM planning, MCP tool execution, and final summarization. Existing LLM caching techniques s
Abstract:
arXiv:2605.20630v1 Announce Type: new Abstract: Industrial asset operations workflows are latency-sensitive because a single user query may require coordination over sensor data, work orders, failure modes, forecasting tools, and domain-specific agents. We evaluate this problem on AssetOpsBench (AOB), an industrial agent benchmark whose plan-execute pipeline exposes repeated overhead from tool discovery, LLM planning, MCP tool execution, and final summarization. Existing LLM caching techniques s
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