Automated structural testing of LLM-based agents: methods, framework, and case studies

TL;DR

This paper introduces a structural testing framework for LLM-based agents using traces, mocking, and assertions, enabling automated, in-depth testing and faster root-cause analysis to improve agent quality.

Contribution

It presents novel methods for automated structural testing of LLM agents, integrating software engineering practices into AI agent testing workflows.

Findings

Automated testing reduces costs and increases coverage.

Faster root-cause analysis with trace-based debugging.

Enhanced defect detection earlier in development.

Abstract

LLM-based agents are rapidly being adopted across diverse domains. Since they interact with users without supervision, they must be tested extensively. Current testing approaches focus on acceptance-level evaluation from the user's perspective. While intuitive, these tests require manual evaluation, are difficult to automate, do not facilitate root cause analysis, and incur expensive test environments. In this paper, we present methods to enable structural testing of LLM-based agents. Our approach utilizes traces (based on OpenTelemetry) to capture agent trajectories, employs mocking to enforce reproducible LLM behavior, and adds assertions to automate test verification. This enables testing agent components and interactions at a deeper technical level within automated workflows. We demonstrate how structural testing enables the adaptation of software engineering best practices to agents, including the test automation pyramid, regression testing, test-driven development, and multi-language testing. In representative case studies, we demonstrate automated execution and faster root-cause analysis. Collectively, these methods reduce testing costs and improve agent quality through higher coverage, reusability, and earlier defect detection. We provide an open source reference implementation on GitHub.