AI-Powered Software Engineering

The Windsurf transformation: From uncertainty to success

Our engineering teams have experienced a remarkable transformation in their approach to software development, particularly through their adoption of Windsurf as an AI coding assistant. The shift from traditional tools represents more than just a technology upgrade; it has fundamentally changed how engineers think about problem-solving and productivity. In this article, three engineers discuss their perspectives from their software and quality backgrounds.

What changed and why it matters

The transition to Windsurf revealed several critical advantages that teams are now leveraging daily:

• Architectural Thinking: Unlike previous tools that focused on code completion, Windsurf can engage in high-level architectural discussions and implement complex solutions end-to-end.
• Contextual Understanding: The AI maintains context throughout more extended conversations, allowing for iterative refinement and learning during implementation.
• Rapid Tool Evolution: Windsurf's AI has been rapidly evolving, with engineers reporting that prompts that failed just weeks ago now generate significantly better code and solutions.

Real-world impact: The photo hosting success story

One of the most compelling demonstrations of AI's transformative potential came from a seemingly straightforward challenge: hosting photos from a third-party registration system that didn't provide public Application Programming Interface (API) access.

Software Engineer Steve Lewis identifies this experience as a turning point in his relationship with AI tools. The challenge involved creating infrastructure to manually download photos from a User Interface (UI), host them publicly, and serve them through the mobile application. This complex task required integrating AWS S3, CloudFront, and custom domains, while also addressing security considerations.

The traditional approach vs. AI-assisted development

Without AI, this project would have involved: 

• Weeks of documentation research for CloudFront and S3 configurations
• Trial-and-error learning for Terraform Infrastructure as Code
• Separate learning curve for Secure Sockets Layer (SSL) certificate management and Route 53 integration
• Manually developing upload scripts and error handling
• Estimated timeline: One week minimum

With Windsurf assistance, the actual outcome was: 

• Thirty-minute architectural discussion (with Windsurf) to settle on the optimal approach
• Automated generation of Terraform modules with an explanation of each setting
• Complete SSL certificate and custom domain setup with guided implementation
• Production-ready Node.js upload script with comprehensive error handling and progress reporting
• Final timeline: One and a half days 

Upskilling revolution: Learning while doing

David Roff's experience represents one of the most profound impacts of AI tools: the ability to become productive in unfamiliar technologies while simultaneously learning them.

As a Software Engineer, David had never done backend development before joining his project. Through AI assistance, he's been able to contribute meaningfully to full-stack development while learning new technologies and patterns in real time.

The crucial mindset ahift 

This perspective yields several key practices:

• Quality Control: Treating AI output as code from a junior engineer ensures proper review and validation.
• Learning Opportunities: Each AI-generated solution becomes a teaching moment for understanding new concepts.
• Best Practices: Human judgment remains essential for architectural decisions and coding standards.
• Iterative Improvement: Continuous questioning and refinement of AI suggestions lead to better outcomes.

Quality engineering revolution: Testing innovations

Tracy Fischer's exploration of AI applications in quality assurance revealed unexpected opportunities for improving testing workflows and documentation processes. He found that Windsurf could analyze the entire codebase to provide test coverage insights without requiring expensive third-party tools. 

Automated test documentation and coverage analysis

Tracy discovered that Windsurf could generate comprehensive manual regression tests for mobile applications, often exceeding initial requirements by including additional testing scenarios:

• Security Testing: AI-generated tests covered more security considerations than were explicitly requested.
• Connectivity Scenarios: Tests covered various network conditions and offline functionality.
• Accessibility Coverage: Windsurf generated additional testing scenarios for accessibility compliance.
• Multiple Model Comparison: Using different AI models within Windsurf provided varying levels of detail and accuracy.

The "double developer" effect

Tracy observed what he terms the "double developer effect" on team dynamics: "It feels like there were eight developers that I was working with instead of four because software engineers on the project really dove into Windsurf and were moving cards across the board really fast."

This acceleration created both opportunities and challenges:

• Increased Velocity: Development teams could complete features much faster than traditional timelines.
• QA Bottlenecks: The single QA engineer became overwhelmed with the increased output.
• Adaptive Strategies: Teams developed "swarming" approaches where developers regularly assisted with testing.
• Workflow Evolution: Traditional team composition ratios may need reconsideration.

The dependency question: When AI becomes essential

Perhaps the most honest and essential discussion centered on the reality of AI dependency: the team acknowledged that AI tools have become essential to their current productivity levels.

Velocity and expectations

The team observed this dependency firsthand when Windsurf became temporarily unavailable. As Steve Lewis described: "That was the first wake-up call for me—wow, this made you realize how much we were relying on it."

Managing dependency risks

The leadership perspective, shared by Area Director Geoff Armstrong, emphasizes proactive dependency management:

• Tool Diversification: Ensuring multiple AI coding tools are available and vetted
• Vendor Relationships: Maintaining strong partnerships with AI tool providers
• Credit Management: Developing sustainable usage patterns within budget constraints and re-evaluating often
• Quality Through Experience: Leveraging senior engineers' judgment to guide AI tool usage

Cultural transformation: A tool that evolves

Tracy Fischer captured something unique about the current moment in AI adoption: "This is the first time in my career … that I've used a tool that is literally changing as I'm using it."

This observation underscores the dynamic nature of AI tool adoption, where the tools themselves continually evolve through user feedback and technological advancements. Unlike traditional development tools that remain static once deployed, AI systems create an ongoing partnership between human expertise and evolving artificial capability.

Key takeaways for engineering organizations

These experiences revealed several critical insights for organizations considering or expanding AI tool adoption:

• Train Engineers: Provide training on Windsurf so engineers can start using it.
• Knowledge Sharing: Leverage AI Roundtables to share experiences and advice.
• Seek Project Approval: Work with each project/client to obtain approval for Windsurf.
• Competitive Strategy: Position AI adoption as amplifying human expertise rather than replacing it.

The path forward

The engineers who shared their experiences aren't just early adopters; they're developing the best practices and mindsets that will define effective AI-assisted software development.

The success stories demonstrate clear patterns: AI tools excel at accelerating learning, compressing research time, generating robust implementations, and enabling engineers to work effectively in unfamiliar domains. However, the most successful implementations maintain human judgment as the guiding force, treating AI as a powerful junior engineer that requires experienced oversight and direction.

As more teams adopt these approaches, the engineering industry is witnessing a fundamental shift in how software gets built, who can build it, and how quickly complex problems can be solved. The organizations that learn to harness this transformation while maintaining quality and security standards will define the next era of software development excellence.

These insights represent ongoing discussions within our engineering community. As AI tools continue evolving and our teams develop new practices, we'll continue sharing learnings and refining our approaches to maximize the benefits while managing the challenges of this transformative technology.