Testing Chatbot Systems using Agentic AI Approach

Abstract

As large language models (LLMs) become increasingly integrated into real-world applications, robust and scalable evaluation methods are essential to ensure their reliability, safety, and effectiveness. This work introduces an innovative evaluation framework grounded in an agentic AI simulation approach, designed to overcome the limitations of traditional testing methodologies in newly developed chatbots. Unlike conventional methods that depend on static benchmarks or human evaluators, our approach employs autonomous AI agents capable of simulating a wide spectrum of user interactions. Within a controlled multi-agent environment, these evaluator agents interact with the target chatbot using natural language queries specifically designed to probe various functional capabilities, identify edge cases, and uncover potential failure modes. The agentic evaluation methodology systematically assesses the performance of chatbots in multiple dimensions, including task completion efficiency, contextual understanding in dynamic conversations, and adherence to safety and ethical guidelines. By incorporating recent advances in agentic metrics and automated scenario generation, our system produces detailed data-driven performance reports that capture both strengths and vulnerabilities in chatbot behavior. Preliminary results show that this approach not only reveals significantly more edge cases than conventional methods, but also reduces overall evaluation time by approximately 60-70 percent. This work contributes to a scalable, standardized testing paradigm that better aligns theoretical performance indicators with the practical challenges of deploying LLMs in real-world environments.