The digital era demands a redefinition of Quality Assurance (QA) from a reactive process to a proactive, human-centric discipline. This approach integrates AI-driven automation, sustainability, security, and ethical practices to deliver resilient and meaningful user experiences.

The following five pillars outline this transformation:

1. AI & Agentic Automation
   AI is evolving from an accelerator to an autonomous collaborator, generating test cases, self-healing scripts, and predicting failures. This reduces manual effort while requiring robust governance and explainability frameworks.
2. Shift-Left Testing
   Testing is no longer an end-of-cycle activity. By embedding QA into design and architecture stages, organizations enable real-time feedback loops and automated quality gates, accelerating releases without compromising trust.
3. Sustainable & Responsible Testing
   QA teams now share responsibility for environmental impact. Carbon-aware pipelines, optimized test suites, and energy-efficient cloud practices reduce compute waste and align testing with corporate sustainability goals.
4. Security & Privacy through DevSecOps
   Security testing shifts both left and right—integrated early and monitored continuously. AI enhances vulnerability detection and compliance, ensuring that trust and regulatory adherence remain uncompromised.
5. Human-Centric & Ethical QA
   While automation scales, humans define what “quality” means. Empathy, accessibility, and ethical AI decision-making ensure that technology serves people, not just processes, creating experiences that are inclusive and trustworthy.

In essence, Human-Centric Quality combines intelligence, responsibility, and empathy to build testing ecosystems that are AI-driven, sustainable, secure, and deeply human.

As someone deeply engaged in the evolution of quality engineering, I’ve seen firsthand how traditional automation is struggling to keep up with the scale, speed, and complexity of today’s digital systems. In this session, I’ll share why I believe the future lies in autonomous testing—a smart, self-directed approach powered by Agentic AI, self-healing frameworks, and GenAI-driven test generation.
I’ll walk you through the key differences between rule-based automation and truly autonomous systems that can learn, adapt, and optimize test strategies in real time. We’ll explore the benefits, risks, and real-world considerations that engineering, and quality leaders must navigate when moving toward autonomy.
I’ll also highlight how this evolution supports business-critical operations, using United Parcel Service brokerage and customs clearance systems as a real-world example. In a global logistics environment where compliance, real-time decisioning, and cross-border visibility are essential, software failures are not an option. Autonomous testing ensures that these complex systems remain resilient, responsive, and agile—even under constantly changing regulatory and operational conditions.

• My goal is to provide a clear understanding of what it takes to begin your own autonomous testing journey—and how it can transform not just QA practices, but broader business outcomes.

AI technology is ubiquitous and playing a critical role in decision making in all the modern applications across varied domains. As technologist we are embracing AI to improve our productivity and AI integration goes very deep into the system architecture. But we need to be aware of the fact that AI models or LLM can collapse over a period of time. Model outcome quality can degrade over a period of time. Blind trust in algorithmic outputs can lead to biased judgments, ethical violations, and critical errors with far-reaching consequences. This session emphasizes the necessity of cultivating the role of the AI verifier—a professional or organizational function dedicated to scrutinizing, validating, and monitoring AI systems.

• Importance of AI verifier role
• Real value of human data to keep the LLMs sane
• Model verification and continuous improvement
• Mindset transformation needed to change from traditional QA to AI verifier

Shift-left is no longer just “testing early.” It is a cultural and engineering transformation that ensures quality is built into every stage of product development, not inspected in at the end. This session introduces a clear, modern interpretation of shift-left—one that emphasizes early test design, stronger collaboration between developers and QA, meaningful quality gates in pull requests, and continuous feedback throughout the development lifecycle.

We will explore practical patterns such as requirement-level test thinking, API-first validation, contract testing, unit and component test maturity, and performance considerations that start before code is even merged. The talk highlights how teams can reduce rework, prevent late-stage defects, shorten release cycles, and build predictable delivery pipelines by adopting a disciplined shift-left approach.

Participants will walk away with a concrete, actionable blueprint for implementing shift-left in their own teams, along with the cultural behaviors, engineering practices, and process enablers required to make early quality ownership a reality.

Participants will gain a practical understanding of what shift-left truly means today and how to integrate quality early across requirements, design, pull requests, and development workflows. They will learn how to establish strong PR quality gates, reduce rework through early test design and API-first validation, and build a developer-first testing culture supported by clear maturity expectations. The session will also outline the cultural behaviors teams need to adopt for effective early quality ownership, along with measurable KPIs—such as reduced defect escapes and shorter cycle times—to track the success of shift-left initiatives.

Banks, Financial Institutions, Insurance providers and other industries like Retail, Travel and Transportation, Healthcare etc. are modernizing their technology stack to provide outstanding digital customer experience to their end users. In this context, applications are reimagined / migrated to cloud / consumerization of APIs to connect with 3rd party systems, etc. Test automation is playing role of an important lever across the entire Software Test Life Cycle (STLC) to speed up the activities performed. This presentation talks about the ways of achieving test automation in a bigger way (Huge Volumes) for a Variety of technology stack and how to speed up the test execution (Velocity) to deploy the different customers applications to production environment in an accelerated fashion. With the recent GenAI trends, How the GenAI, Artificial Intelligence (AI) / Machine Learning (ML) features would help to contribute to increased test automation coverage which in turn will accelerate the applications migration to production, will be analyzed in detail during this presentation.

Impact of AI and GenAI in Software Test Life Cycle (STLC) and how this will contribute to increase the test automation coverage (Volume), Across different channels (web apps, mobile device, etc.) (Variety), Acceleration of the automated test scripts (thousands of scripts) execution from multiple parallel machines in few hours (Velocity).

This session demonstrates a revolutionary approach to accelerating the software testing lifecycle through an AI-powered agentic pipeline. Traditional test automation workflows require significant manual effort—analysts read user stories, QA engineers craft test cases, and automation engineers write skeleton code. This fragmented process often takes days or weeks, creating bottlenecks in agile delivery. I will present an end-to-end agentic pipeline that transforms Jira user stories into executable test automation skeletons in under 60 minutes.
The system leverages Large Language Models (LLMs) orchestrated through autonomous agents that:
1.Extract and analyse Jira story content, acceptance criteria, and contextual
metadata
2. Generate comprehensive test cases covering positive, negative, boundary, and
edge scenarios using domain-aware reasoning
3. Produce automation skeletons in the team’s preferred framework (Playwright,
Cypress, Selenium, etc.) with proper page object patterns and assertions
The pipeline employs a multi-agent architecture where specialized agents handle
requirements parsing, test scenario generation, code scaffolding, and validation—each agent collaborating autonomously while maintaining traceability back to the original requirements.

This approach reduces manual effort by 70-80%, ensures consistent test coverage, and enables shift-left testing by generating tests as soon as stories are refined. I willdemonstrate the complete workflow live, including integration patterns, prompt
engineering strategies, and quality gates that ensure generated artifacts meet production standards.

Babu Manickam is a visionary technology leader and first-generation entrepreneur with over 26 years of experience across software engineering, large-scale automation, and artificial intelligence. He has held leadership roles at HCL, Syntel, and Hewlett-Packard, leading global delivery and performance engineering teams. As the Founder & CEO of QEagle, Babu is focused on redefining quality engineering through AI-driven and agentic AI solutions, helping enterprises accelerate delivery through reliable, scalable, and high-impact engineering. A passionate mentor and coach, he has mentored over 50,000 professionals across 32 countries.

• Agentic AI Transforms Testing Economics
• Multi-Agent Architecture is Critical
• Traceability is Built-In, Not Bolted-On
• Framework-Agnostic Code Generation
• Human-in-the-Loop Remains Essential
• Prompt Engineering is the New Test Design Skill
• Integration Points Matter
• Quality Gates Prevent AI Hallucinations

• An Industry Expert with nearly 25 years of professional experience in Quality Assurance
• 9+ years of experience in Over The Top (OTT) domain
• 16+ years of multinational business experience focused on Financial Technology (FT) testing services & solutions
• Experience ranging from Web developer, Manual module lead, Automation team lead, QA manager, Program manager, Engagement Manager, Americas Testing Delivery Head, Head of Test Automation CoE to Director of Quality Assurance
• Key strengths in the areas of testing solution architecting, pre-sales positioning, customer liaison and roadmap/strategy consulting for specialty testing centre of excellence, building & grooming team
• Lead multiple parallel projects & programs

As a QA Manager with extensive experience in quality assurance, Mani specializes in leading and optimizing testing processes across diverse industries. He is a Certified ISTQB Technical Test Lead, Scaled Agile Practitioner, and Salesforce Administrator. His expertise spans both manual and automated testing, with a strong focus on UI automation using Playwright, Selenium WebDriver and RESTful API testing using JMeter and Postman. His goal is to ensure that every software release meets the highest quality standards, driving continuous improvement and efficiency in QA processes.

Madan is Certified AI & ML QA Manager and having expertise in AI algorithms, statistics and Tableau. He is an expert in QA strategy and governance for large institutions. Madan is passionate about AI based systems to bring in QA strategy, process and tools for testing the AI systems. He is a graduated from Great Lakes Institute of Management on Business Analytics and Business Intelligence in finance, Supply chain and marketing domains. He believes in trust based relationship and acquiring persuading and story telling skills.