Gathering Porto’s QA & Tech Community
Gathering Porto’s QA & Tech Community
Community Edition
Open to our entire Porto Tribe, & beyond. Limited seats.
- 9th May, 2026
- Porto
150+ Leading Minds from the Tech & Testing Community,
Driving the Future of Engineering, Quality, and AI in Business.
Testers and Engineers attending from these companies
QonfX
Conversations that matter
Shipping Faster
Responsible AI
Agentic AI
AI in Testing
Leadership
AI Security
Shipping Faster
Responsible AI
Agentic AI
AI in Testing
Leadership
AI Security
Leadership Sessions
Expert Panel
Fireside Chat
Leadership
Networking
Live Demos
What is QonfX?
QonfX is our flagship global conference that brings the QA and Tech community together to explore the Future of Software—Quality, Engineering, AI, and beyond.
Curated to inspire deep conversations, breakthrough insights, and meaningful connections, QonfX offers a high-value learning experience for professionals committed to excellence. Hosted across key cities in the US, Europe, and India, QonfX brings together the minds driving the next evolution of software quality and engineering.
Speakers
- Conference Venue
Porto Business School
- Saturday, 9th May 2026
Conference Topics
Porto
Talk: The Specification is the Code
Pedro Costa
Exploratory Testing
Functional Testing
Porto
Talk: The New Quality Challenge - Speed, AI, and Ownership
Software teams have never moved faster. With AI-powered tools, developers can now generate code, tests, and solutions in seconds — dramatically increasing productivity and accelerating delivery.
But while speed has evolved, quality hasn’t kept up.
This creates a dangerous illusion: the belief that more output means better outcomes.
In many organizations, what is perceived as a strong quality culture is often just a collection of tools, processes, and checklists — a “quality façade.” And with the rise of AI, this façade becomes even more convincing, as automatically generated code and tests give a false sense of confidence.
In this talk, Joana Silva explores what true quality culture really means and why it’s more critical than ever in an AI-driven world. She will challenge common assumptions about quality, highlight the risks of over-reliance on AI, and explain how speed can unintentionally increase technical debt, defects, and loss of trust.
Through practical insights and real-world experience, this session will cover how organizations can move beyond the illusion of quality and build a culture where quality is truly owned by everyone.
Because in the age of AI, one thing becomes clear:
AI doesn’t replace quality culture — it amplifies it.The software engineering world is currently trapped in the “Happy Path Fallacy” when it comes to AI. We build an agent, test a few isolated prompts in a playground, verify the “vibes,” and push to production. But traditional testing methodologies break down with AI. Agents are non-deterministic, they take multi-step cognitive trajectories, and they suffer from silent drift when the underlying models update. For engineering and QA teams, “vibe checks” do not scale.
To build reliable AI systems, we need to transition from the dark art of ad-hoc prompt crafting into a rigorous engineering discipline. This session is designed for technical leaders and engineers who need to bring predictability to unpredictable models. We will explore how to build automated evaluation pipelines that act as the ultimate CI/CD gatekeeper for GenAI applications, treating evaluations as the immutable product specification.
We will dive deep into a practical Eval Toolkit comprising four scalable patterns: Deterministic Assertions for strict formatting, LLM-as-a-Judge for open-ended quality (and how to overcome its inherent biases), Trajectory Evaluations for workflow efficiency, and Adversarial Evals for stress-testing edge cases.
By anchoring these metrics in comprehensive Tracing (using tools like MLflow), teams can finally embrace “Eval-Driven Development” (EDD). This talk provides a concrete, code-backed approach to stop guessing, catch regressions automatically, and turn real-world production failures into permanent test cases.
Joana Silva
Exploratory Testing
Functional Testing
Porto
Talk: Real-World AI: Lessons from Production Environments
Deploying Generative AI in production is fundamentally different from building demos or experimenting with prompts. What works in controlled settings often breaks down when systems must operate reliably at scale, handle unpredictable inputs, and integrate with real users and business processes.
In this talk, we’ll share practical lessons from building production-grade GenAI systems, covering prompt design, tool calling, guardrails, human-in-the-loop and evaluation (evals) for non-deterministic models. We’ll also touch on monitoring and observability in real-world environments.
We’ll highlight common pitfalls—such as prompt fragility, lack of systematic evals, and underestimated token costs—through.
The goal is to cut through the GenAI hype and provide a pragmatic view of what it takes to build and operate reliable AI systems in production.
Ricardo Filipe
Exploratory Testing
Functional Testing
Porto
Talk: Evaluating AI Agents: From Prompt Craft to Engineering Discipline
The software engineering world is currently trapped in the “Happy Path Fallacy” when it comes to AI. We build an agent, test a few isolated prompts in a playground, verify the “vibes,” and push to production. But traditional testing methodologies break down with AI. Agents are non-deterministic, they take multi-step cognitive trajectories, and they suffer from silent drift when the underlying models update. For engineering and QA teams, “vibe checks” do not scale.
To build reliable AI systems, we need to transition from the dark art of ad-hoc prompt crafting into a rigorous engineering discipline. This session is designed for technical leaders and engineers who need to bring predictability to unpredictable models. We will explore how to build automated evaluation pipelines that act as the ultimate CI/CD gatekeeper for GenAI applications, treating evaluations as the immutable product specification.
We will dive deep into a practical Eval Toolkit comprising four scalable patterns: Deterministic Assertions for strict formatting, LLM-as-a-Judge for open-ended quality (and how to overcome its inherent biases), Trajectory Evaluations for workflow efficiency, and Adversarial Evals for stress-testing edge cases.
By anchoring these metrics in comprehensive Tracing (using tools like MLflow), teams can finally embrace “Eval-Driven Development” (EDD). This talk provides a concrete, code-backed approach to stop guessing, catch regressions automatically, and turn real-world production failures into permanent test cases.
Luís Manuel Maia
Exploratory Testing
Functional Testing
Porto
Talk: The Testing Job Market After AI - Who Survives?
AI is no longer a side topic in software engineering. It is changing how software is built, how fast it moves, and how value is perceived across teams. As software becomes increasingly developed with AI and shaped by AI-driven capabilities, the testing profession is being pushed into a new reality, one where old assumptions about roles, career paths, and relevance are no longer safe.
This talk explores that shift in three moments.
1st, it frames why AI matters now, not as hype, but as a force already altering the economics and speed of software delivery.
2nd it brings that pressure into the testing job market, where rising expectations, automation fatigue, and new AI-enabled practices are creating both anxiety and opportunity.
3rd , it asks the question many testers, leaders, and teams are already feeling: what kind of testing professional will remain valuable in the next few years?
Rather than offering an apocalyptic message or an overly optimistic one, this session takes a pragmatic view of a profession in transition.
It looks at the growing tension between technology acceleration and professional adaptation, and challenges the audience to rethink what relevance, contribution, and differentiation mean in the age of AI.
Attendees will leave with a clearer understanding of the forces reshaping testing careers, a sharper perspective on the changes already underway, and a stronger sense of what it will take to stay relevant in an industry that is moving faster than ever.
Paulo José E. V. Matos
Exploratory Testing
Functional Testing
Porto
Talk: From Automation to Intelligence: Evolving Quality Practices for Reliable Software Delivery
The ongoing advancement of software engineering has underscored the vital role of quality
processes in delivering reliable and high-performing software. For the software quality assurance
community, the challenge is not only to ensure that solutions meet functional and non-functional
requirements, but also to embed quality checks throughout every phase of the lifecycle — from
requirements and design through coding, integration, deployment and operations.
Quality assurance is not confined to testing alone. Each phase presents unique risks and
opportunities for improvement, and early detection of issues is far more cost-effective than late-
stage fixes. Regulatory compliance adds another layer of complexity, as many industries (such
as finance, healthcare and automotive) require traceability, auditability and documented
evidence of quality controls at every stage. The selection of the appropriate development
methodology is also critical: waterfall may be preferable for projects with stable requirements
and strict regulatory demands, while agile is often chosen for its flexibility and rapid feedback in
dynamic environments. In large programmes with multiple teams, the presence of dedicated
software quality engineers (SQE) professionals and robust quality control mechanisms is
essential to ensure consistency, coordination and compliance across all workstreams.
Selecting the most effective approaches and technologies for quality assurance directly impacts
the efficiency and accuracy of all development activities. Established practices such as
automated testing, continuous integration and metrics-driven management remain foundational,
yet their success depends on thoughtful adaptation to the unique needs of each project and
team.
Artificial intelligence (AI) is increasingly reshaping the landscape of software engineering by
automating complex and repetitive tasks, enhancing defect detection, and enabling predictive
analytics. AI-powered tools can generate test cases, prioritise test execution, and analyse large
volumes of results, freeing teams to focus on exploratory and value-added activities. The
possibility of using AI for automation is particularly significant, as it allows for the rapid creation
and maintenance of scripts, intelligent selection of scenarios, and dynamic adaptation to changes
in requirements or code. Machine learning models can identify patterns in defect or operational
data, helping teams anticipate risk areas and allocate resources more effectively. Additionally,
natural language processing can support requirements analysis and documentation, reducing
misunderstandings and improving collaboration across teams. Integrating AI into quality
processes offers the potential to accelerate cycles, improve coverage, and support continuous
feedback across the entire development lifecycle.
However, the successful adoption of AI and other advanced technologies requires careful
evaluation of organisational readiness, data quality and ethical considerations. Teams must
assess the maturity of their current processes, the compatibility of new tools with existing
workflows, and the skills required to operate and maintain AI-driven solutions. Addressing
concerns such as data privacy, model transparency and algorithmic bias is essential to ensure
that AI applications support, rather than undermine, quality objectives. Ongoing training and
change management are critical to building confidence and competence within the organisation
as new technologies are introduced.
Dalila Branco
Exploratory Testing
Functional Testing
Porto
Panel: The Craft After AI: What Testers Actually Do Now
AI is in the codebase. Copilot writes tests. Agents run exploratory scripts. So what do testers actually do now? This panel brings together practitioners from Bosch, DevExperts and Mindera to talk about how the daily work of testing has changed, not whether jobs exist, but what fills the hours. Are testers reviewing AI output, writing better prompts, or shifting to risk analysis? We will hear what is actually happening on the ground.
Simão Belchior de Castro
Manuel Oliveira
Dmitry Derbenev
Exploratory Testing
Functional Testing
More talks coming soon...
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Frequently Asked Questions
Yes, QonfX Porto is an in-person conference only.
QonfX is designed for testing and technology professionals, with a strong focus on the future of tech and testing. Anyone from the tech and testing community is welcome to attend.
To register, you can simply click here. Although limited slots are available are granted on a first-come, first-served basis.
No—in-person attendees must use their reserved invites. To ensure fairness, do not apply unless you can commit to attending.
QonfX Porto will be held on May 9, 2026, from 9:30AM WEST.
Thanks for your interest! Once you register, you’ll receive a confirmation email along with a calendar invite to block your day. As we get closer to the event, we’ll update the invite with the venue details and continue sharing all relevant information over email.
To cancel your pass and request a refund, please notify us at [email protected] at least 15 days prior to the event.
No, session recordings will not be shared, as QonfX is an in-person event only.
Yes, you will receive a Participation Certificate on your registered email.
We appreciate your interest in contributing to QonfX! Here’s how you can get involved:
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