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Six Reliability Primitives for LLM Agents: An Artifact Pattern for Stackable, Single-Concern Libraries

Authors: Katta, Mukunda Rao;

Six Reliability Primitives for LLM Agents: An Artifact Pattern for Stackable, Single-Concern Libraries

Abstract

Reliability concerns for large-language-model agents are typically addressed inside frameworks that bundle prompting, tool routing, and runtime governance into a single dependency. This paper presents the agent-stack: a set of six small, single-concern reliability libraries published independently to npm, PyPI, and the Model Context Protocol registry. The libraries are AgentFit (context-window fitting), AgentGuard (network egress allowlist), AgentSnap (snapshot tests for tool-call traces), AgentVet (validate tool arguments before execution), AgentCast (structured-output enforcer), and AgentBudget (token and dollar caps). Each library is zero-dependency, has a TypeScript implementation with hand-maintained type declarations, a Python port with the same surface, and an MCP-server variant. Repository inspection across the eighteen public packages identifies a recurring artifact pattern: a single class or function as the public surface, a typed error carrying retry-friendly context, and an opt-in automatic adapter for popular provider response shapes. The contribution is a documented design pattern for reliability primitives that compose by inclusion rather than by framework lock-in. The paper describes the primitives, the cross-cutting invariants the design enforces, the trade-offs of single-concern packaging, and the operational questions that emerge when reliability is split across many small dependencies.

Keywords

Model Context Protocol, reliability primitives, agent infrastructure, LLM agents, TypeScript, Python

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average