Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines
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 such as KV-cache reuse and embedding-based semantic caching were designed for chatbot serving and bre
The rapid development and deployment of LLMs in industrial settings are exposing latency and efficiency bottlenecks, necessitating immediate solutions like advanced caching for agentic workflows.
Improving the efficiency of agentic AI pipelines is crucial for their commercial viability and widespread adoption across latency-sensitive industrial applications, directly impacting productivity and operational agility.
Optimized AI agent workflows become more practical for real-time industrial applications, reducing operational overhead and accelerating decision-making processes facilitated by AI.
- · Industrial operators using AI
- · AI agent developers
- · Cloud providers offering optimized AI services
- · Companies with inefficient AI inference architectures
- · Manual workflow integrators
Increased adoption of AI agents in mission-critical industrial asset operations due to improved performance.
Greater demand for specialized AI infrastructure and middleware capable of optimizing complex agentic workflows.
Acceleration of 'lights-out' operations and fully autonomous industrial processes as AI agents become more reliable and efficient.
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Read at arXiv cs.AI