Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines
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| Format: | Preprint |
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2026
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| author | Merchant, Alimurtaza Mustafa Veera, Krish Goyla, Sajal Kumar Bhure, Shambhawi Patel, Dhaval Maghraoui, Kaoutar El |
| author_facet | Merchant, Alimurtaza Mustafa Veera, Krish Goyla, Sajal Kumar Bhure, Shambhawi Patel, Dhaval Maghraoui, Kaoutar El |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_20630 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines Merchant, Alimurtaza Mustafa Veera, Krish Goyla, Sajal Kumar Bhure, Shambhawi Patel, Dhaval Maghraoui, Kaoutar El Artificial Intelligence I.2.11; H.3.4; C.4 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. |
| title | Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines |
| topic | Artificial Intelligence I.2.11; H.3.4; C.4 |
| url | https://arxiv.org/abs/2605.20630 |