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This research aims to evaluate the effectiveness of different LLM architectures in financial data governance.

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April 13, 2026Open Access

DataGov-Factory: An Empirical Evaluation of Managed vs. Custom LLM Agent Architectures for Financial Data Governance

Key Points

This research aims to evaluate the effectiveness of different LLM architectures in financial data governance.
Controlled empirical comparison of three LLM deployment architectures
Evaluation of platform-managed agents
Assessment of custom multi-agent factory
Analysis of direct API calls
Platform-level prompt caching reduces context costs by approximately 90%
Managed agents achieve 16% higher quality compared to other architectures

Abstract

This paper presents the first controlled empirical comparison of three LLM deployment architectures for financial data governance: (A) platform-managed agents, (B) custom multi-agent factory, and (C) direct API calls. Key finding: platform-level prompt caching reduces context costs by ~90%, making managed agents competitive while delivering 16% higher quality. v2: Fixed broken LaTeX references.

DataGov-Factory: An Empirical Evaluation of Managed vs. Custom LLM Agent Architectures for Financial Data Governance

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Abstract

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