AI & Intelligent Testing - How AI is Redefining Test Automation and Execution

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Speaker: Taran Agrawal | LinkedIn

Topic: AI & Intelligent Testing - How AI is Redefining Test Automation and Execution

Agenda:
🕓 4:00–4:05 PM PST — Welcome
🕓 4:05–4:55 PM PST — Taran Agrawal | LinkedIn - AI & Intelligent Testing: How AI is Redefining Test Automation and Execution
🕓 4:55–5:00 PM PST — Q&A & Closing

Summary:
AI is transforming software testing, creating new ways for teams to automate testing workflows, connect tools, and improve testing efficiency.

In this session, Taran Agrawal will explore how AI chatbots can support the shift from traditional testing processes to an AI-driven, end-to-end testing ecosystem. Attendees will learn how AI can connect internal knowledge bases, testing tools, and automation frameworks to generate, execute, and self-heal test scripts while improving team productivity.

The session will also introduce key architectural concepts behind intelligent testing, including Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and AI agents. Taran will explain how these technologies work together to connect organizational knowledge with testing tools and workflows.

The discussion will cover practical applications across business, functional, and automation testing, including test case management, test execution, test data generation, defect management, and handling flaky tests. Attendees will also explore how AI can support the development of a Test Center of Excellence (TCoE), improve CI/CD testing pipelines, and address data security in AI-powered testing environments.

Takeways:
What attendees will learn:

1. How AI and machine learning are shaping the future of software testing and influencing testing requirements, job roles, and market demand.
2. How AI can be applied across business, functional, and automation testing.
3. How AI can help organizations establish and evolve a Test Center of Excellence (TCoE).
4. The fundamentals of RAG, MCP, and AI agents, and how they work together to connect internal knowledge, tools, and AI capabilities.
5. How to integrate tools such as APIs, Confluence, Jira, Jenkins, GitHub, and databases with an AI chatbot.
6. How AI chatbots can work with test cases, test sets, test runs, and other testing activities.
7. How specialized AI agents can assist with test script creation, test execution, self-healing, test data creation and population, and defect creation and management.
8. How AI can help identify and manage flaky tests and intermittent failures.
9. How to approach data security when integrating AI and machine learning into testing workflows.
10. Why static testing remains important in modern software testing.

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