What question did this study set out to answer?

This research aims to optimize the serving of agentic workflows involving Large Language Models (LLMs) by addressing inefficiencies in current systems.

May 20, 2026Proceedings of the ACM on Management of Data

Efficient LLM Serving for Agentic Workflows: A Data Systems Perspective

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Authors

NWNoppanat WadlomNational University of Singapore JSJunyi ShenNational University of Singapore YLYao LuNational University of Singapore

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Implication

Randomized trial shows up to 1.56× speedup in LLM workflows, suggesting end-to-end optimization is vital for efficiency.

Key Points

This research aims to optimize the serving of agentic workflows involving Large Language Models (LLMs) by addressing inefficiencies in current systems.
Introduced Helium, a workflow-aware serving framework for LLM invocations.
Integrated proactive caching and cache-aware scheduling to enhance prompt reuse.
Modelled agentic workloads as query plans to leverage classic query optimization.
Achieved up to 1.56× speedup over existing agent serving systems.
Demonstrated improved efficiency across various workloads through end-to-end optimization.

Cite This Study

Wadlom et al. (2026) studied this question.

synapsesocial.com/papers/6a0d4f34f03e14405aa9a667 https://doi.org/10.1145/3802046

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Efficient LLM Serving for Agentic Workflows: A Data Systems Perspective

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Key Points

Cite This Study

Efficient LLM Serving for Agentic Workflows: A Data Systems Perspective

Discussion

Member takes

Implication

Key Points

Cite This Study

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