Tom Smykowski is a product-minded staff software engineer with nearly two decades of experience in product delivery, specializing in setting up AI coding agents and AI-native software development lifecycles. Hiring Tom for AI coding agents setup means engaging an expert who combines hands-on engineering with strategic product thinking to implement, optimize, and scale AI-assisted development workflows, directly boosting your team's productivity and code quality. He is the ideal partner for founders, CTOs, and executives seeking to modernize their development process with ownership from discovery through production.
What is AI Coding Agents Setup?
AI coding agents setup involves integrating intelligent, automated tools into your software development workflow. These agents, powered by large language models and specialized frameworks, assist with code generation, review, testing, and refactoring. The goal is to create a more efficient, consistent, and high-quality development process, often referred to as an AI software factory.
Proper setup goes beyond simply installing a plugin. It requires a strategic assessment of your current SDLC, selection of appropriate tools like Claude Code, Cursor, or GitHub Copilot, and the creation of custom workflows, prompts, and quality gates. This ensures the agents augment your team's capabilities rather than disrupt them, leading to tangible improvements in velocity and reduction in technical debt.
For teams in the EU, UK, and US collaborating with remote engineers, a well-configured AI agent system also fosters better alignment and knowledge sharing. It creates a common, intelligent layer that understands project context and enforces best practices, making distributed collaboration smoother and more effective.
The Strategic Value of an AI-Assisted Workflow
Implementing AI coding agents is a strategic investment in your engineering organization's future. The immediate benefit is accelerated development cycles, but the long-term value lies in enhanced code consistency, improved onboarding for new hires, and the ability to tackle more complex problems with confidence. It shifts your team's focus from repetitive coding tasks to higher-value architecture and innovation.
Tom's approach, honed through roles like Staff Software Engineer at Croissant and leading frontend at industrial SaaS company Fream, is to align the AI setup with business outcomes. Whether preparing for a major event like the Printify rebrand ahead of its merger or building scalable systems for thousands of businesses at Websoul, the focus is always on delivering maintainable, user-focused products. AI agents become force multipliers in achieving these goals.
This is particularly crucial for remote and distributed teams. A shared AI toolset acts as a always-available senior pair programmer, providing guidance and ensuring standards are met regardless of time zone. For a Poland-based engineer like Tom, working with US East Coast or UK teams, these tools bridge collaboration gaps and maintain a high bar for code quality across the entire development lifecycle.
Tom's Methodology for Implementation
Tom's methodology for AI coding agents setup is a phased, consultative process tailored to your team's maturity and goals. It begins with a discovery audit of your current codebase, development practices, and team structure. This assessment identifies the highest-impact areas for AI integration, whether it's accelerating feature development, improving test coverage, or automating code reviews.
The next phase involves tool selection and configuration. With deep expertise in modern stacks including Vue.js, TypeScript, and full-stack environments, Tom evaluates tools like Cursor, Claude Code, and Copilot against your specific tech stack and workflow. He then creates custom agent profiles, prompt libraries, and integration scripts to weave these tools seamlessly into your existing CI/CD pipeline and IDEs.
The final and most critical phase is enablement and iteration. Tom believes setup is only the beginning. He provides hands-on training, creates documentation, and establishes metrics to measure the impact on developer productivity and code quality. This ensures your team not only adopts the new tools but also evolves their usage over time, continuously refining the AI-assisted workflow for maximum benefit.
Case Studies in Modernization and Scale
Tom's real-world experience provides a blueprint for successful AI integration. At Printify, he led the design system rebrand impacting over 60 frontend engineers and a platform serving 10M+ merchants. In such a large-scale environment, the principles of consistency, automation, and clear standards are paramount. An AI coding agents setup in this context would focus on enforcing design system patterns, automating UI component generation, and ensuring accessibility compliance at scale.
As the founder of Websoul, which served 32,000+ businesses with marketing automation and fintech SaaS, Tom built products from the ground up using Vue, TypeScript, and Supabase. For a startup or scale-up context, AI agents accelerate MVP development, ensure robust backend logic with AG Grid integrations, and help maintain code quality during rapid growth phases. The setup prioritizes speed without sacrificing the foundation needed for future scale.
His work with clients like GE Healthcare, Unilever, and Bunge demonstrates an ability to navigate complex, regulated domains. Here, AI agent setup includes stringent quality gates, audit trails for AI-generated code, and a focus on security and compliance. This risk-aware approach ensures that the power of AI is harnessed responsibly, meeting the high standards expected by enterprise and industrial clients.
Integrating AI into Your Existing Tech Stack
A common concern is how AI tools will interact with an existing, often complex, technology stack. Tom's extensive background across frontend, backend, and full-stack development allows him to design integrations that feel native. Whether your team uses Angular, React, Node.js, or Python, the setup process involves creating tailored context providers, custom plugins, and workflow automations that respect your architecture.
For instance, integrating AI code review agents into a CI/CD pipeline requires understanding the build process, test suites, and deployment gates. Tom configures agents to run automated analyses, suggest improvements, and even generate fix PRs based on the specific patterns and libraries your application uses. This turns the AI into a specialized member of your team, fluent in your codebase's unique dialect.
Furthermore, Tom emphasizes the importance of the human-in-the-loop. The setup is designed to provide suggestions and automation, not to make autonomous decisions that could break production. This balance empowers engineers, boosts their confidence in the AI's output, and fosters a culture of collaborative intelligence where the team and the tools work in concert.
When This is a Good Fit
Engaging Tom for AI coding agents setup is an excellent fit if you are a founder, CTO, or engineering lead at a startup, scale-up, or established company looking to significantly improve your development velocity and code quality. It is ideal if you have a remote or distributed team across the EU, UK, or US and need a unified, intelligent workflow to enhance collaboration. This service is also highly valuable if you are undertaking a modernization project, scaling a SaaS platform, or building a new AI-native product and want to bake best-practice AI tooling into your foundation from the start.