[2605.20173] A Methodology for Selecting and Composing Runtime Architecture Patterns for Production LLM Agents

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Computer Science > Artificial Intelligence

arXiv:2605.20173 (cs)

[Submitted on 19 May 2026]

Title:A Methodology for Selecting and Composing Runtime Architecture Patterns for Production LLM Agents

Authors:Vasundra Srinivasan<br>View a PDF of the paper titled A Methodology for Selecting and Composing Runtime Architecture Patterns for Production LLM Agents, by Vasundra Srinivasan

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Abstract:Production LLM agents combine stochastic model outputs with deterministic software systems, yet the boundary between the two is rarely treated as a first-class architectural object. This paper names that boundary the stochastic-deterministic boundary (SDB): a four-part contract among a proposer, verifier, commit step, and reject signal that specifies how an LLM output becomes a system action. We argue that the SDB is the load-bearing primitive of production agent runtimes.

Around this primitive, we organize agent runtime design into three concerns: Coordination, State, and Control. We present a catalog of six runtime patterns that compose the SDB differently across conversational, autonomous, and long-horizon agents: hierarchical delegation, scatter-gather plus saga, event-driven sequencing, shared state machine, supervisor plus gate, and human in the loop. For each pattern, we trace its lineage to distributed-systems concepts and identify what changes when the worker is stochastic.

The paper contributes a five-step methodology for selecting runtime patterns, a diagnostic procedure that maps production failures to pattern weaknesses, and a failure mode called replay divergence, in which LLM-based consumers of a deterministic event log produce different downstream outputs under model-version or prompt changes. A stylized reliability decomposition separates per-call model variance from architectural momentum, motivating the claim that as model variance decreases, pattern choice and SDB strength become increasingly important levers for long-run reliability. We apply the methodology to five workloads and provide one runnable reference implementation for a 90-day contract-renewal agent.

Comments:<br>25 pages, 2 figures, 6 tables. Companion repo at this https URL

Subjects:

Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

Cite as:<br>arXiv:2605.20173 [cs.AI]

(or<br>arXiv:2605.20173v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.20173

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Vasundra Srinivasan [view email]<br>[v1]<br>Tue, 19 May 2026 17:54:21 UTC (29 KB)

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