Abstract
Performance testing is essential for contemporary software systems because it ensures reliability and user satisfaction (while keeping operational costs under control). Conventional performance testing activities still require expert knowledge, laborious scripting, and substantial time investment within modern DevOps workflows. Recent advances in generative and agentic AI create the opportunity to automate these processes. This work-in-progress paper explores the use of agentic AI to support end-to-end automation of software performance testing. We present an initial prototype that coordinates multiple specialized agents to extract contextual information from existing project artifacts, generate realistic performance test scenarios, execute tests using an established performance testing framework, and provide structured interpretations of the resulting metrics. We report early results from exploratory case studies of microservice-based systems from the DeathStarBench benchmark suite, which suggest that agentic workflows can autonomously produce executable performance tests and meaningful performance reports with limited human intervention, even when available context is incomplete or partially misleading. Such results illustrate the feasibility of agent-based performance testing and motivate a broader research agenda on AI-augmented performance engineering.