Agent-Testing Agent: A Meta-Agent for Automated Testing and Evaluation of Conversational AI Agents
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914002233196544 |
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| author | Komoravolu, Sameer Mrini, Khalil |
| author_facet | Komoravolu, Sameer Mrini, Khalil |
| contents | LLM agents are increasingly deployed to plan, retrieve, and write with tools, yet evaluation still leans on static benchmarks and small human studies. We present the Agent-Testing Agent (ATA), a meta-agent that combines static code analysis, designer interrogation, literature mining, and persona-driven adversarial test generation whose difficulty adapts via judge feedback. Each dialogue is scored with an LLM-as-a-Judge (LAAJ) rubric and used to steer subsequent tests toward the agent's weakest capabilities. On a travel planner and a Wikipedia writer, the ATA surfaces more diverse and severe failures than expert annotators while matching severity, and finishes in 20--30 minutes versus ten-annotator rounds that took days. Ablating code analysis and web search increases variance and miscalibration, underscoring the value of evidence-grounded test generation. The ATA outputs quantitative metrics and qualitative bug reports for developers. We release the full methodology and open-source implementation for reproducible agent testing: https://github.com/KhalilMrini/Agent-Testing-Agent |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_17393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agent-Testing Agent: A Meta-Agent for Automated Testing and Evaluation of Conversational AI Agents Komoravolu, Sameer Mrini, Khalil Computation and Language Artificial Intelligence LLM agents are increasingly deployed to plan, retrieve, and write with tools, yet evaluation still leans on static benchmarks and small human studies. We present the Agent-Testing Agent (ATA), a meta-agent that combines static code analysis, designer interrogation, literature mining, and persona-driven adversarial test generation whose difficulty adapts via judge feedback. Each dialogue is scored with an LLM-as-a-Judge (LAAJ) rubric and used to steer subsequent tests toward the agent's weakest capabilities. On a travel planner and a Wikipedia writer, the ATA surfaces more diverse and severe failures than expert annotators while matching severity, and finishes in 20--30 minutes versus ten-annotator rounds that took days. Ablating code analysis and web search increases variance and miscalibration, underscoring the value of evidence-grounded test generation. The ATA outputs quantitative metrics and qualitative bug reports for developers. We release the full methodology and open-source implementation for reproducible agent testing: https://github.com/KhalilMrini/Agent-Testing-Agent |
| title | Agent-Testing Agent: A Meta-Agent for Automated Testing and Evaluation of Conversational AI Agents |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2508.17393 |