Continuous Testing for State-Based Multistep Voice AI Agents

Citation: Vladyslav Budichenko, "Continuous Testing for State-Based Multistep Voice AI Agents", Universal Library of Engineering Technology, Volume 02, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a strategy for continuous testing of multi-step AI agents based on finite-state machines (FSM) and integrated with large language models (LLMs). The primary focus is on validating the correctness of transitions (both deterministic and LLM-driven) and evaluating the quality of responses, including testing formats such as voice-to-voice, voice-to-text, and text-to-text. It is demonstrated that the state-based approach, where each FSM node is treated as an independent “module” for testing, allows for error localization and effective application of quality metrics (e.g., G-Eval) in a continuous mode. Additionally, methods for integrating a knowledge base (RAG) into test scenarios and organizing integration and end-to-end (e2e) testing are discussed. The proposed practical recommendations are illustrated through the example of “call booking” and can be extended to more complex voice and text-based dialogue systems.

Keywords: Continuous Testing, Multi-Step LIM Agents, Finite-State Machines, State-Based Testing, Voice-To-Voice / Voice-To-Text / Text-To-Text, G-Eval, Knowledge Base / RAG, Unit Testing, Integration Testing, End-To-End (e2e) Testing.

https://doi.org/10.70315/uloap.ulete.2025.0201005