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

TL;DR

This paper evaluates caching and workflow optimization techniques in industrial agent pipelines, demonstrating significant speedups and identifying limitations of semantic caching for parameter-rich queries.

Contribution

It introduces a temporal semantic cache and MCP workflow optimizations, improving efficiency and analyzing caching limitations in industrial workflows.

Findings

Semantic caching can achieve up to 30.6x speedup on cache hits.

Workflow optimizations reduced median latency by 40%.

Pure semantic caching can fail on parameter-rich 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.