Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines

2026-05-19

AI Summary

This work addresses the high latency faced by industrial agents on the AssetOpsBench benchmark, which stems from repeated overhead in tool discovery, LLM-based planning, and MCP execution. Traditional semantic caching proves ineffective in scenarios sensitive to time, asset, or sensor parameters. To overcome this limitation, the authors propose a time-aware semantic caching mechanism that integrates disk-backed tool discovery caching with a dependency-aware parallel execution strategy, thereby optimizing the plan–execute pipeline. Experimental results demonstrate that the approach reduces end-to-end median latency by approximately 40% and accelerates MCP workflows by up to 1.67×. Notably, when time-aware cache hits occur, median speedups reach as high as 30.6×, underscoring the inherent limitations of purely semantic caching in complex industrial queries.

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 such as KV-cache reuse and embedding-based semantic caching were designed for chatbot serving and break down when output validity depends on time, asset, or sensor parameters. We propose two complementary optimization layers for AOB plan-execute pipelines: a temporal semantic cache and a set of MCP workflow optimizations combining disk-backed tool-discovery caching and dependency-aware parallel step execution. MCP workflow optimizations corresponded to a 1.67x speedup and reduced median end-to-end latency by about 40.0% while the temporal-cache benchmark achieved a median of 30.6x speedup on cache hits. Beyond the speedup, our results expose a concrete failure mode of pure semantic caching for parameter-rich industrial queries, providing a critical analysis of how caching choices interact with evaluation correctness in MCP-backed agent benchmarks.

Problem

• Temporal Semantic Caching
• Workflow Optimization
• Plan-Execute Pipeline
• Industrial Agents
• LLM Caching

Innovation

• temporal semantic caching
• MCP workflow optimization
• plan-execute pipeline
• industrial agent benchmark
• dependency-aware parallel execution