QonfX 2025 - Key Topics at This Year’s Conference - The Test Tribe

What attendee will learn this year?

Here are the Topics

All

London

Singapore

Hyderabad

London

The AI Testing Maturity Trap: What Two Real Rollouts Taught Me

“AI has unlocked real possibilities across functional and non-functional test automation from intelligent test generation to self-healing suites to AI-assisted performance and security testing. The second edition of Full Stack Testing maps this landscape as a practical, beginner-friendly guide. But knowing what’s possible and knowing where your organization actually stands are different problems and that gap is where most AI testing initiatives quietly stall.

This talk bridges the two. Drawing on two real client engagements from my work as an independent technology consultant, I’ll unpack what a practical AI testing maturity curve looks like the capabilities available at each stage, what each stage actually demands of an organization, and the missteps that stalled the rollout. You’ll leave with a simple framework to assess where your team sits today and what the next stage truly requires.”

Gayathri Mohan

Gayathri Mohan

London

Managing AI Agents Like People: Autonomy, Feedback, and Trust

This talk explores why AI agents create a leadership challenge, not just a tooling challenge. Ugo will show how familiar management disciplines – clear outcomes, scoped autonomy, feedback loops, guardrails, review, and accountability – apply when agents become part of the SDLC. The session gives engineering and quality leaders a practical way to adopt AI agents without losing judgement, quality, or ownership.

ugo anomelechi headshot

Ugo Anomelechi

London

Quality Engineering for Probabilistic Software
“We spent 30 years perfecting quality engineering for deterministic software. Now AI has changed the rules. My premise is that LLM evals aren’t a completely new discipline, they’re the natural evolution of quality engineering, where confidence comes from statistical evidence, continuous evaluation, and engineering systems that learn rather than simply pass or fail. The talk explores how familiar quality engineering concepts such as regression testing, coverage, release gates, observability, and production monitoring map to modern evaluation practices for AI-powered systems. Rather than focusing on a particular framework or tool, it provides a practical engineering mindset for building confidence in probabilistic software, highlighting both the lessons we can carry forward from traditional QE and the new challenges that emerge when software is no longer deterministic. “
Christopher hughes 1

Christopher hughes

Singapore

Why Human Judgment Is the Most Valuable Intelligence in an Agentic World
As AI agents increasingly move from assisting humans to reasoning, deciding, and acting autonomously, the role of humans is fundamentally changing. This session explores why human judgment not information, intelligence, or execution will become the critical differentiator in an agentic world.
SG Madhur

Madhur Mayank Sharma

Singapore

AI-Native Engineering: When Development Speeds Up, Quality Must Speed Up Too

AI is accelerating how engineering teams build, review, and automate work. In this session, I’ll share 8×8’s AI-native engineering transformation journey and how we are applying AI across development workflows, productivity, support automation, and operational efficiency.

I’ll also discuss what this speed means for quality engineering: why quality, security, and governance controls must evolve at the same pace, and how QA teams can contribute through smarter automation, risk-based validation, and evaluation frameworks for AI-powered products and bots.

Anton

Anton Shchekalov

Singapore

Verification Is the Moat: QA in the agentic era

Boris Cherny, the creator of Claude Code at Anthropic, was asked what separates the people who get great results out of AI coding agents from everyone else. His answer had nothing to do with clever prompts. He said verification is probably the single most important thing that people do not get right. Give the model a way to check its own work, he said, and the quality of what comes back goes up two to three times. Around the same time, the researcher Jason Wei described a simple rule of thumb: the easier a task is to check, the faster AI gets good at it. Put those two ideas side by side and you get an uncomfortable picture. Anything easy to check will eventually be done by machines. What stays with us is the work that is genuinely hard to check. Generation became cheap. Knowing whether the output is actually right did not.

That is the job this talk is about, and in practice it comes down to two things. The first is evals. When a feature is powered by AI there is often no single correct answer to assert against, so the old test case with a fixed expected value stops working. An eval is simply how you check something that has no one right answer, and writing evals is quietly becoming part of the QA role rather than a data science speciality. The second is what happens to the testing lifecycle itself once agents do the work. I will walk through an Agent-driven Test Development Life Cycle, or ATDLC: the same stages every QA team already knows, from understanding the requirement through planning, case design, automation and reporting, except an agent drafts each stage and a human has to approve it before the next one can start. We will look at the real artifacts each stage produces, so it stays concrete rather than theoretical. We will also be honest about where all of this breaks, including judge models that quietly prefer their own answers, and a controlled study in which experienced developers using AI were 19% slower while believing they had been 20% faster. You will leave knowing what to try on Monday, whether you write the tests or lead the team that does.

SG Amit

Amit Rawat

Singapore

What We Learned Shipping AI across 1000+ Engineers

AI is changing software engineering faster than most teams can adapt. At Wise, we have been integrating AI into an organisation of over 10,000 people, including more than 1,000 engineers. Not through demos, but in production, at scale, and in a heavily regulated industry.

This talk is an honest look at what we have learned: how we are rethinking testing and quality, how we are measuring whether AI is actually improving engineering productivity, and the challenges of scaling AI across an organisation of this size: from governance and accountability to cost and engineering practices.

No transformation frameworks or AI hype. Just practical lessons on what’s working, what did not, and what engineering leaders can take back to their teams.

SG Jose 1 1 1

Jose Galarza

Singapore

Panel: Building & Scaling in the AI Era

AI is rewriting the rules faster than companies can plan. Engineering and QA teams are being restructured some roles disappearing, others emerging, skills becoming obsolete within quarters. Leaders are making decisions with less certainty than ever: when technology evolves faster than your planning cycle, how do you commit to a direction? What do CEOs actually expect from their tech and quality leaders now? And underneath it all, the question nobody wants to answer honestly  what happens to the people whose jobs AI can do faster and cheaper? This panel brings the CEO perspective to a room full of the people they’re hiring, restructuring, and betting on

SG Geoff

Geoff Ira

SG Will Fan

Will Fan

Amrita Mishra

Amrita Mishra

SG Yong Wong

Yong Jie Wong

SG Priyanka Kapur

Priyanka Kapur

Singapore

Panel: AI in Quality: What's Actually Working

Everyone’s adding AI to their workflows but what’s actually delivering results and what’s just noise? This panel gets practical: what tools are worth adopting, what skills teams need to build, and what’s quietly failing that nobody talks about on stage. Two practitioners share what changed when they brought AI into how they ship software what broke before, what works now, and what they’d do differently. Whether you’re in QA, engineering, product, or building AI systems yourself if you’re responsible for shipping reliable software faster, this is the session to take notes.

SG Ameya Khasgiwala

Ameya Khasgiwala

SG Amit

Amit Rawat

SG Maheeja Manepalli

Maheeja Manepalli

Saikat Mukherjee

Saikat Mukherjee

Hyderabad

Talk: Engineering & Leadership Excellence in the Age of AI

The rise of intelligent systems is reshaping what it means to build, scale, and lead in technology. Engineering leaders today must deliver at unprecedented speed while ensuring every layer—architecture, data, and human decision-making—remains trustworthy. Charu unpacks how the craft of engineering is evolving as AI becomes an active participant in design, testing, and delivery. She discusses how to lead teams that balance experimentation with discipline, translate complexity into clarity, and build systems that improve with every release. The session offers a forward look at how excellence in 2026 will be defined not just by performance, but by responsibility, resilience, and the ability to lead through constant change.

Charu Srinivasan

Charu Srinivasan

Exploratory Testing

Functional Testing

Hyderabad

Talk: Agentic Systems — The Next Step in Autonomous Engineering & Quality

Engineering is entering a new phase where systems don’t just follow instructions — they take initiative. Jaydeep introduces how agentic AI is changing the way teams design, build, and validate software. He explains what happens when AI begins coordinating tasks, analysing code, suggesting fixes, and influencing release decisions. The session focuses on where these systems help, where they can go wrong, and what leaders must do to keep quality and reliability in check. Expect a practical look at using agentic capabilities responsibly while still maintaining clarity, control, and confidence across engineering and QA workflows.

Jaydeep Chakrabarty

Jaydeep Chakrabarty

Exploratory Testing

Functional Testing

Hyderabad

Talk: Leading for Quality in the Age of AI

As AI becomes embedded in products and processes, the meaning of “quality” is shifting. Vanya focuses on how leaders can redefine quality for their teams — not as a phase, but as a shared mindset that guides decisions, behaviours, and standards. She explores how to create clarity around expectations, build cross-functional ownership, and shape cultures where speed doesn’t override judgment. This talk gives leaders a forward-looking view of how quality evolves when AI influences design, delivery, and user experience — and how strong leadership can keep that quality visible, consistent, and intentional.

Vanya Seth

Vanya Seth

Exploratory Testing

Functional Testing

Hyderabad

Talk: Platform Engineering to achieve Quality with Speed

Modern engineering teams are expected to deliver fast without letting reliability slip. This talk explores how platform engineering provides the foundation to achieve both. Swetha breaks down how well-designed platforms remove friction, improve visibility, and create predictable paths to production. She explains how teams can use shared services, guardrails, and strong feedback loops to keep quality consistent even as AI-driven development accelerates delivery. The session focuses on practical steps leaders can take to build platforms that bring stability, speed, and confidence to engineering teams.

Shwetha

Swetha Yalamanchili

Exploratory Testing

Functional Testing

Hyderabad

Talk: From Test Automation to Intelligent Quality: Unlocking Business Value with AI

Most organizations have automated testing but struggle to translate it into tangible business impact. With AI, the next evolution isn’t more automation-it’s AI-powered intelligent quality engineering.
This session explores how AI and GenAI redefine testing through predictive defect detection, AI-driven test generation, autonomous maintenance, and adaptive quality gates that learn from production data. By shifting focus from test execution metrics to business value and release confidence, QA engineering teams can move from reactive testing to proactive quality assurance that drives measurable outcomes.

Pankaj Kumar

Pankaj Kumar

Exploratory Testing

Functional Testing

Hyderabad

Fireside Chat: Leading Through Change: How Leaders Build Resilient Teams

Change brings opportunity, but it also brings hesitation, fear, and pushback. In this conversation, Nirmala & Swetha talk openly about the real challenges leaders face when driving new automation or AI initiatives — from resistance on the ground to moments when teams simply aren’t ready. They discuss how to handle tough conversations, address fears of job loss, deal with underperformance, and prevent leaders in the middle from slowing progress.

The session also explores how to upskill teams when capabilities do not match expectations, how to keep people motivated through uncertainty, and how leadership traits have evolved in the AI era. It’s a practical, honest look at what strong, resilient leadership really requires today — clarity, empathy, firmness, and the ability to help teams grow through change rather than fear it.

Nirmala Datta

Nirmala Datla

Shwetha

Swetha Yalamanchili

Exploratory Testing

Functional Testing

Hyderabad

Panel Discussion: Accelerating Quality with AI — The Leadership Playbook

AI is changing how engineering teams design, build, and release software — and leaders now face a new question: how do you increase speed without letting quality slip? This panel brings together leaders who work at the intersection of product, platform, and AI-driven delivery. They’ll discuss how teams can use AI to shorten feedback loops, improve decisions, and uncover risks earlier, while still keeping reliability and user experience front and center. The conversation will focus on practical leadership habits that make AI useful every day — from setting the right expectations to choosing the right problems, and helping teams balance ambition with discipline. It’s a straightforward playbook for delivering faster without losing trust.

Jaydeep Chakrabarty

Jaydeep Chakrabarty

Prajakt Deshpande

Prajakt Deshpande

Shiva Kumar R V

Shiva Kumar RV

Exploratory Testing

Functional Testing

Hyderabad

Panel Discussion: Quality in AI - Testing the omnipresent unpredictable

AI systems behave differently from traditional software — they learn, adapt, and sometimes work in ways teams didn’t anticipate. This panel looks at how to bring clarity and control to systems that are always shifting, through three essential lenses: platform engineering built on scalable, open frameworks; data-science methods for observing model behaviour and drift; and product-scale rollout strategies that keep reliability front of mind. The conversation will cover how to test behaviour that isn’t deterministic, how to prepare for failures no one scripted, and how to build trust in AI-rich products using open foundations and real-world practice.

Sourabh

Saurabh Mitra

Shravan

Shravan Koninti

Shashank pic removebg preview Picsart AiImageEnhancer

Shashank Chaturvedi

Exploratory Testing

Functional Testing

Further details coming soon..