Toward Reliable Design of LLM-Enabled Agentic Workflows: Optimizing Latency-Reliability-Cost Tradeoffs
This paper analyzes the fundamental tradeoffs among latency, reliability, and cost in LLM-enabled agentic workflows. It introduces performance models using a parametric exponential reliability function for LLM agents and proposes a water-filling token allocation policy under latency and cost constraints.
Article intelligence
Key points
- LLM agentic workflows involve tradeoffs among latency, reliability, and cost.
- A parametric exponential reliability function models LLM agent performance.
- A water-filling token allocation policy optimizes reliability under constraints.
Why it matters
This matters because LLM agentic workflows involve tradeoffs among latency, reliability, and cost.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.23929] Toward Reliable Design of LLM-Enabled Agentic Workflows: Optimizing Latency-Reliability-Cost Tradeoffs
[Submitted on 21 Apr 2026]
Title:Toward Reliable Design of LLM-Enabled Agentic Workflows: Optimizing Latency-Reliability-Cost Tradeoffs
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Abstract:Modern AI systems increasingly rely on workflows composed of multiple interacting agents, some powered by large language models (LLMs) and others by conventional computational modules. This paper analyzes the fundamental tradeoffs between latency, reliability, and cost in LLM-enabled agentic workflows. We introduce performance models for both LLM and non-LLM agents that capture the relationship between computational effort and output quality, incorporating the impact of reasoning and output tokens for LLM agents using a parametric exponential reliability function. Then, we study the design of sequential workflows under latency and cost constraints. Main results include a water-filling token allocation policy and characterizations of optimal workflow reliability in terms of shadow prices.
Subjects:
Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as: arXiv:2605.23929 [cs.AI]
(or arXiv:2605.23929v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.23929
arXiv-issued DOI via DataCite
Submission history
From: Ya-Ting Yang [view email] [v1] Tue, 21 Apr 2026 23:09:16 UTC (646 KB)
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