How to Test With Agentic AI automation Tool - Geosley Andrades

The podcast explores the impact of agentic automation AI on quality assurance (QA) teams, emphasizing its role in transforming traditional testing practices. Agentic AI leverages large language models (LLMs) to automate tasks like test case generation, synthetic data creation, and code writing, reducing reliance on deep coding expertise. Key features include autonomous discovery, where AI analyzes application interfaces to identify issues, and self-healing capabilities that adapt to evolving applications, minimizing maintenance burdens. However, challenges persist in the testing industry, such as skill gaps in coding and AI, maintenance complexities with conventional frameworks, and scalability issues in manual processes. To address these, experts recommend starting with small AI implementations, collaborating with domain experts, and investing in training to bridge knowledge gaps.

The discussion highlights the need for strategic AI adoption, focusing on tools that support end-to-end testing across web, APIs, and enterprise systems (e.g., Salesforce, SAP) while prioritizing design principles and contextual intelligence to avoid hallucinations. Misconceptions about AI include over-reliance on automation without proper validation or context, which can lead to flawed outcomes. Effective use requires balancing AI-generated outputs with human oversight, ensuring alignment with business logic and testing rigor. Enterprise applications face unique challenges, such as frequent UI updates disrupting automation scripts, which agentic platforms like ExcelQ address through live asset updates and pre-built accelerators. The conversation also underscores the importance of metrics like bug quality, automation efficiency, and ROI evaluation to guide AI tool selection and implementation.

Finally, the podcast stresses the evolving role of QA professionals in an AI-driven era. While agentic systems accelerate testing, human expertise remains critical for prompt engineering, validating AI outputs, and understanding design principles. Career advice emphasizes continuous learning, adaptability, and mastering AI-specific concepts (e.g., RAG pipelines, vector databases). Testers are encouraged to embrace AI as a tool to enhance efficiency rather than replace their roles, focusing on contextual awareness and fast-paced testing to align with modern development cycles. The shift from traditional methods to AI-integrated testing demands a balance between automation and human judgment, ensuring robust, scalable QA frameworks.