Avindra Fernando
Principal Architect | Fractional CTO | AI-Driven Development & Modernization | Conference Speaker
Kansas City, Missouri, United States
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Avindra Fernando is a software consultant with over 13 years of experience helping teams build scalable, modern web applications. In 2021, he founded Taprobane Consulting to guide clients through architectural decisions, migrations to newer tech stacks, and the implementation of robust AI-powered development and testing strategies that ensure quality and performance.
He actively mentors engineering teams, leads workshops, and delivers hands-on training. As a regular speaker at tech conferences, Avindra shares practical lessons on concepts and technologies such as Software Architecture, AI-driven development, React, Next.js, E2E Testing, and TypeScript. He leverages his experience to shape future technology direction and accelerate the growth of companies across industries.
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Give Your AI Agents a Playbook
Using AI agents without clear guidance can feel like walking in the dark. One prompt works well. The next one takes you somewhere completely different. As agents start doing more work inside our codebases, how do we give them the context and structure they need?
I’ve been using skills and playbooks to make that work more repeatable. Skills give an agent reusable instructions and context for a specific kind of work. Playbooks lay out a repeatable process for tasks like building a feature, reviewing a pull request, or writing tests. Instead of explaining the same things over and over, the agent can load the right guidance when it needs it. This approach can work across tools like Claude Code, Codex, and Cursor.
In this session, I will show how I structure skills and playbooks, decide what belongs in each, and keep the context focused. We will also look at common problems like conflicting instructions, stale guidance, and agents choosing the wrong workflow. You will leave with practical ideas you can use to move beyond one-off prompts and give agents a more repeatable way to work in your codebase.
The Future of Code Reviews: Will Humans Still Be Needed?
What if AI reviewed your pull requests before a human ever looked at them? Today AI agents can scan diffs, suggest fixes, and catch bugs in seconds. So what does that mean for human code reviews?
Tools like CodeRabbit, Copilot, and Qodo AI are already helping teams review code faster. AI can point out issues, suggest improvements, and flag risky changes. But good software is not just about catching bugs. Context, architecture decisions, and long-term maintainability still require human judgment.
In this session we’ll explore how AI and human reviewers can work together in modern development workflows. You’ll learn where AI can speed things up, where humans still matter most, and how teams can rethink the review process going forward.
Building Better Tests: Lessons from Real World E2E Testing
Many companies use end-to-end testing to release high-quality software and stay competitive. With tools like Playwright and Cypress, testing web applications has become easier and more enjoyable.
As a software consultant, I have faced challenges that can make or break an E2E testing strategy. Slow tests caused by time-based waits, flaky failures due to async behavior, and brittle tests from unreliable selectors were just a few. Through experience, I have learned how to overcome these issues and help my clients build tests that are faster, more stable, and maintainable.
In this session, I will share real-world lessons and simple techniques to avoid common testing pitfalls. You will walk away with practical strategies to write reliable tests and improve the overall quality of your applications.
The XR Takeover: Is the Web Browser Era Ending?
In our fast-changing world, Extended Reality (XR) is becoming the next big thing. It’s like stepping into a new kind of world where things are more than they seem. We’re already seeing XR in many areas, such as gaming, shopping, and even software development.
But what if XR could change the way we use the internet? Today, we use web browsers to build software, shop, and search for information. Now imagine stepping into a 3D world where you don’t just look at content, but interact with it. Devices like smart glasses and XR headsets are already showing us what’s possible. They offer experiences that go beyond what traditional browsers can do.
So, what can you expect from this talk? We’ll look at how XR might reshape how we use the internet. Will XR replace web browsers, or will they work together? Let’s explore these questions and see where XR is headed.
Building the AI-first Developer Stack
AI has changed how we write software, but have we actually changed how we build it? Many teams have added coding agents and AI tools to the same development process they were already using. What would it look like to redesign that workflow around AI from the start?
In my work as a principal architect and consultant, I have been refining an AI-first way of working that starts well before the code. The workflow starts with refining requirements and reviewing a plan, then moves through implementation, code review, testing, and validation. Different agents can take on different parts of the process, while clear instructions, constraints, and human judgment keep the work on track.
In this session, I will walk through the workflow and show what it looks like in practice. I will share what has worked, what has not, and where people still need to stay involved. You will leave with practical ideas for building an AI-first development process that helps your team move faster without losing control of the software you are building.
Playbook-Based E2E Testing with Playwright CLI and AI Agents
End-to-end tests are powerful, but they can be slow to write and easy to drift. We start with requirements, turn them into test code, and over time the original intent can get lost. What if we could keep that intent in plain language and give AI agents a repeatable way to turn it into reliable E2E coverage?
I’ve been using playbooks to give AI agents a repeatable way to work through E2E testing. The playbook tells the agent how to gather the right context, turn requirements into clear scenarios, use Playwright CLI to work through the flow, and validate the result. The goal is not to generate more tests. It is to give the agent a repeatable process for creating the right ones.
In this session, I will show how the playbook works in practice, where AI helps, and where people still need to stay involved. We will look at how requirements, designs, and existing code can feed into the process, and how the results are checked before they become part of the test suite. You will leave with a practical way to use AI for E2E testing without losing control of the behavior your tests are supposed to protect.
Coding Isn’t the Bottleneck. Validation Is.
AI can generate code in seconds. We can create features, tests, and pull requests faster than ever. But if writing the code is getting easier, what is the real bottleneck?
I’ve seen the challenge shift from generating code to knowing whether that code is actually good. Does it solve the right problem? Does it fit the architecture? Can we trust it in production? As AI helps teams produce more code, testing, review, clear constraints, and human judgment become even more important.
In this session, we will look at how teams can shift their focus from generation to validation. I will share practical ways to check AI-generated work throughout the development process, catch problems earlier, and decide when the code is actually ready for production.
Beyond Prompting: How Skills and Playbooks Guide AI Agents
Using AI agents without clear guidance can feel like walking in the dark. One prompt works well. The next one takes you somewhere completely different. As agents start doing more work inside our codebases, how do we give them the context and structure they need?
I’ve been using skills and playbooks to give agents more structure. Skills give an agent reusable instructions and context for a specific kind of work. Playbooks lay out a repeatable process for tasks like building a feature, reviewing a pull request, or writing tests. Instead of explaining the same things over and over, the agent can load the right guidance when it needs it. This approach can work across tools like Claude Code, Codex, and Cursor.
In this session, I will show how I structure skills and playbooks, decide what belongs in each, and keep the context focused. We will also look at common problems like conflicting instructions, stale guidance, and agents choosing the wrong workflow. You will leave with practical ideas you can use to move beyond one-off prompts and give agents a more repeatable way to work in your codebase.
Think Global: A Guide to High Performing Global Development Teams
We live in a world where our colleagues may not sit next to us, or be in the same city, or even in the same country. A diverse workforce with varied perspectives, help companies to remain competitive today.
But, geographically dispersed teams also come with a set of challenges. Managing development teams is a hard enough task even when the teams are co-located. When the team members come from different parts of the world, it makes it even harder. Communication may deteriorate, misunderstandings may occur, teams may stop trusting each other.
Over the years, I have worked with several global development teams. We have experienced many challenges during our interactions and have figured out ways to overcome them.
In this talk, I would like to share various approaches that I have used in my career to build great relationships with global colleagues. This session will enable you to be a part of a high performing team irrespective of the geographical and cultural differences.
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Avindra Fernando
Principal Architect | Fractional CTO | AI-Driven Development & Modernization | Conference Speaker
Kansas City, Missouri, United States
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