AI Assisted Coding: Transactional AI Development - Commit, Validate, and Rollback With Sergey Sergyenko
Scrum Master Toolbox Podcast: Agile storytelling from the trenches27 Nov

AI Assisted Coding: Transactional AI Development - Commit, Validate, and Rollback With Sergey Sergyenko

AI Assisted Coding: Treating AI Like a Junior Engineer - Onboarding Practices for AI Collaboration

In this special episode, Sergey Sergyenko, CEO of Cybergizer, shares his practical framework for AI-assisted development built on transactional models, Git workflows, and architectural conventions. He explains why treating AI like a junior engineer, keeping commits atomic, and maintaining rollback strategies creates production-ready code rather than just prototypes.

Vibecoding: An Automation Design Instrument

"I would define Vibecoding as an automation design instrument. It's not a tool that can deliver end-to-end solution, but it's like a perfect set of helping hands for a person who knows what they need to do."

Sergey positions vibecoding clearly: it's not magic, it's an automation design tool. The person using it must know what they need to accomplish—AI provides the helping hands to execute that vision faster. This framing sets expectations appropriately: AI speeds up development significantly, but it's not a silver bullet that works without guidance. The more you practice vibecoding, the better you understand its boundaries. Sergey's definition places vibecoding in the evolution of development tools: from scaffolding to co-pilots to agentic coding to vibecoding. Each step increases automation, but the human architect remains essential for providing direction, context, and validation.

Pair Programming with the Machine

"If you treat AI as a junior engineer, it's very easy to adopt it. Ah, okay, maybe we just use the old traditions, how we onboard juniors to the team, and let AI follow this step."

One of Sergey's most practical insights is treating AI like a junior engineer joining your team. This mental model immediately clarifies roles and expectations. You wouldn't let a junior architect your system or write all your tests—so why let AI? Instead, apply existing onboarding practices: pair programming, code reviews, test-driven development, architectural guidance. This approach leverages Extreme Programming practices that have worked for decades. The junior engineer analogy helps teams understand that AI needs mentorship, clear requirements, and frequent validation. Just as you'd provide a junior with frameworks and conventions to follow, you constrain AI with established architectural patterns and framework conventions like Ruby on Rails.

The Transactional Model: Atomic Commits and Rollback

"When you're working with AI, the more atomic commits it delivers, more easy for you to kind of guide and navigate it through the process of development."

Sergey's transactional approach transforms how developers work with AI. Instead of iterating endlessly when something goes wrong, commit frequently with atomic changes, then rollback and restart if validation fails. Each commit should be small, independent, and complete—like a feature flag you can toggle. The commit message includes the prompt sequence used to generate the code and rollback instructions.

This approach makes the Git repository the context manager, not just the AI's memory. When you need to guide AI, you can reference specific commits and their context. This mirrors trunk-based development practices where teams commit directly to master with small, verified changes. The cost of rollback stays minimal because changes are atomic, making this strategy far more efficient than trying to fix broken implementations through iteration.

Context Management: The Weak Point and the Solution

"Managing context and keeping context is one of the weak points of today's coding agents, therefore we need to be very mindful in how we manage that context for the agent."

Context management challenges current AI coding tools—they forget, lose thread, or misinterpret requirements over long sessions. Sergey's solution is embedding context within the commit history itself. Each commit links back to the specific reasoning behind that code: why it was accepted, what iterations it took, and how to undo it if needed. This creates a persistent context trail that survives beyond individual AI sessions. When starting new features, developers can reference previous commits and their context to guide the AI. The transactional model doesn't just provide rollback capability—it creates institutional memory that makes AI progressively more effective as the codebase grows.

TDD 2.0: Humans Write Tests, AI Writes Code

"I would never allow AI to write the test. I would do it by myself. Still, it can write the code."

Sergey is adamant about roles: humans write tests, AI writes implementation code. This inverts traditional TDD slightly—instead of developers writing tests then code, they write tests and AI writes the code to pass them. Tests become executable requirements and prompts. This provides essential guardrails: AI can iterate on implementation until tests pass, but it can't redefine what "passing" means. The tests represent domain knowledge, business requirements, and validation criteria that only humans should control. Sergey envisions multi-agent systems where one agent writes code while another validates with tests, but critically, humans author the original test suite. This TDD 2.0 framework (a talk Sergey gave at the Global Agile Summit) creates a verification mechanism that prevents the biggest anti-pattern: coding without proper validation.

The Two Cardinal Rules: Architecture and Verification

"I would never allow AI to invent architecture. Writing AI agentic coding, Vibecoding, whatever coding—without proper verification and properly setting expectations of what you want to get as a result—that's the main mistake."

Sergey identifies two non-negotiables. First, never let AI invent architecture. Use framework conventions (Rails, etc.) to constrain AI's choices. Leverage existing code generators and scaffolding. Provide explicit architectural guidelines in planning steps. Store iteration-specific instructions where AI can reference them. The framework becomes the guardrails that prevent AI from making structural decisions it's not equipped to make. Second, always verify AI output. Even if you don't want to look at code, you must validate that it meets requirements. This might be through tests, manual review, or automated checks—but skipping verification is the fundamental mistake. These two rules—human-defined architecture and mandatory verification—separate successful AI-assisted development from technical debt generation.

Prototype vs. Production: Two Different Workflows

"When you pair as an architect or a really senior engineer who can implement it by himself, but just wants to save time, you do the pair programming with AI, and the AI kind of ships a draft, and rapid prototype."

Sergey distinguishes clearly between prototype and production development. For MVPs and rapid prototypes, a senior architect pairs with AI to create drafts quickly—this is where speed matters most. For production code, teams add more iterative testing and polishing after AI generates initial implementation. The key is being explicit about which mode you're in. The biggest anti-pattern is treating prototype code as production-ready without the necessary validation and hardening steps. When building production systems, Sergey applies the full transactional model: atomic commits, comprehensive tests, architectural constraints, and rollback strategies. For prototypes, speed takes priority, but the architectural knowledge still comes from humans, not AI.

The Future: AI Literacy as Mandatory

"Being a software engineer and trying to get a new job, it's gonna be a mandatory requirement for you to understand how to use AI for coding. So it's not enough to just be a good engineer."

Sergey sees AI-assisted coding literacy becoming as fundamental as Git proficiency. Future engineering jobs will require demonstrating effective AI collaboration, not just traditional coding skills. We're reaching good performance levels with AI models—now the challenge is learning to use them efficiently. This means frameworks and standardized patterns for AI-assisted development will emerge and consolidate. Approaches like AAID, SpecKit, and others represent early attempts to create these patterns. Sergey expects architectural patterns for AI-assisted development to standardize, similar to how design patterns emerged in object-oriented programming. The human remains the bottleneck—for domain knowledge, business requirements, and architectural guidance—but the implementation mechanics shift heavily toward AI collaboration.

Resources for Practitioners

"We are reaching a good performance level of AI models, and now we need to guide it to make it impactful. It's a great tool, now we need to understand how to make it impactful."

Sergey recommends Obie Fernandez's work on "Patterns of Application Development Using AI," particularly valuable for Ruby and Rails developers but applicable broadly. He references Andrey Karpathy's original vibecoding post and emphasizes Extreme Programming practices as foundational. The tools he uses—Cursor and Claude Code—support custom planning steps and context management. But more important than tools is the mindset: we have powerful AI capabilities now, and the focus must shift to efficient usage patterns. This means experimenting with workflows, documenting what works, and sharing patterns with the community. Sergey himself shares case studies on LinkedIn and travels extensively speaking about these approaches, contributing to the collective learning happening in real-time.

About Sergey Sergyenko

Sergey is the CEO of Cybergizer, a dynamic software development agency with offices in Vilnius, Lithuania. Specializing in MVPs with zero cash requirements, Cybergizer offers top-tier CTO services and startup teams. Their tech stack includes Ruby, Rails, Elixir, and ReactJS.

Sergey was also a featured speaker at the Global Agile Summit, and you can find his talk available in your membership area. If you are not a member don't worry, you can get the 1-month trial and watch the whole conference. You can cancel at any time.

You can link with Sergey Sergyenko on LinkedIn.

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Building Self-Awareness in Overly-Technical Product Owners | Anamaria Ungureanu

Building Self-Awareness in Overly-Technical Product Owners | Anamaria Ungureanu

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1 Aug 14min

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31 Juli 15min

Practical Strategies for Organizational Tool Rollouts | Anamaria Ungureanu

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30 Juli 12min

Knowledge Hoarding and Team Dependencies | Anamaria Ungureanu

Knowledge Hoarding and Team Dependencies | Anamaria Ungureanu

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29 Juli 18min

Goal Clarity—The Missing Piece in Agile Team Performance | Anamaria Ungureanu

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28 Juli 13min

The Hidden Cost of Decision-Making Delays in Product Development | Anh Vu

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25 Juli 12min

Situational Leadership for Scrum Masters - Knowing When to Step Back | Anh Vu

Situational Leadership for Scrum Masters - Knowing When to Step Back | Anh Vu

Anh Vu: Situational Leadership for Scrum Masters - Knowing When to Step Back Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes. Anh defines success for Scrum Masters through a simple but powerful test: when you take vacation and return to find the team working exceptionally well without you. This ultimate measure of success focuses on building self-organization and enabling true team autonomy. He emphasizes that Scrum adoption should serve as a metaphor for autonomy and self-organization, with the Scrum Master's stance evolving alongside the team's growth. Drawing from Situational Leadership principles, Anh points out that a team can only become as autonomous as the Scrum Master can envision and facilitate. The key is recognizing when to step back and allow the team to demonstrate their independence while ensuring they have the foundation and confidence to succeed. Featured Retrospective Format for the Week: Free Form Retrospective Anh advocates for the free form retrospective format, which requires only a whiteboard, table, and people gathering for open reflection based on the previous sprint's goals. Rather than following structured formats, he invites team members to share their insights freely, considering the retrospective successful as long as the team leaves with 1-3 actionable insights. This approach works particularly well with teams he knows well, where trust and communication patterns have been established. He builds on this by incorporating silent thinking time to start conversations, a habit developed over time that makes teams more comfortable sharing in the free form format. Anh also recommends conducting retrospectives during in-office days when the team can eat together, investing heavily in these face-to-face interactions to strengthen team bonds. Self-reflection Question: What would happen if you took a week off from your current role - would your team continue to thrive, or would they struggle without your direct involvement? [The Scrum Master Toolbox Podcast Recommends] 🔥In the ruthless world of fintech, success isn't just about innovation—it's about coaching!🔥 Angela thought she was just there to coach a team. But now, she's caught in the middle of a corporate espionage drama that could make or break the future of digital banking. Can she help the team regain their mojo and outwit their rivals, or will the competition crush their ambitions? As alliances shift and the pressure builds, one thing becomes clear: this isn't just about the product—it's about the people. 🚨 Will Angela's coaching be enough? Find out in Shift: From Product to People—the gripping story of high-stakes innovation and corporate intrigue. Buy Now on Amazon [The Scrum Master Toolbox Podcast Recommends] About Anh Vu Anh Vu is a seasoned Project Manager with 10+ years in software delivery, Agile, DevOps, and PM. As a PMP trainer, he has empowered 200+ professionals. Passionate about continuous learning, Anh explores practical tools and methodologies to help PMs and teams succeed in fast-paced, tech-driven environments. You can link with Anh Vu on LinkedIn.

24 Juli 14min

From Project Mindset to Product Thinking - Leading Client Transformation | Anh Vu

From Project Mindset to Product Thinking - Leading Client Transformation | Anh Vu

Anh Vu: From Project Mindset to Product Thinking - Leading Client Transformation Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes. Anh describes a transformative collaboration experience while building multiple websites for a client. Over time, his team recognized significant commonalities between projects and saw the opportunity to create reusable components for future work. However, they faced resistance when trying to shift the client's mindset from short-term project delivery to long-term product thinking. The business stakeholders remained focused on immediate project completion rather than investing in sustainable, reusable solutions. Anh's approach to leading this change involved presenting concrete evidence from previous projects to demonstrate the tangible benefits of component reusability. Rather than just proposing the idea theoretically, they suggested implementing reusable components immediately within the current project, showing rather than just telling. His strategy centered on providing clear evidence of benefits and demonstrating achievability, making the transition from project to product mindset more tangible and less risky for the client. In this episode, we refer to the book "From Project to Product" by Mik Kersten. Self-reflection Question: How might you help your stakeholders see beyond immediate deliverables to recognize the long-term value of sustainable practices and reusable solutions? [The Scrum Master Toolbox Podcast Recommends] 🔥In the ruthless world of fintech, success isn't just about innovation—it's about coaching!🔥 Angela thought she was just there to coach a team. But now, she's caught in the middle of a corporate espionage drama that could make or break the future of digital banking. Can she help the team regain their mojo and outwit their rivals, or will the competition crush their ambitions? As alliances shift and the pressure builds, one thing becomes clear: this isn't just about the product—it's about the people. 🚨 Will Angela's coaching be enough? Find out in Shift: From Product to People—the gripping story of high-stakes innovation and corporate intrigue. Buy Now on Amazon [The Scrum Master Toolbox Podcast Recommends] About Anh Vu Anh Vu is a seasoned Project Manager with 10+ years in software delivery, Agile, DevOps, and PM. As a PMP trainer, he has empowered 200+ professionals. Passionate about continuous learning, Anh explores practical tools and methodologies to help PMs and teams succeed in fast-paced, tech-driven environments. You can link with Anh Vu on LinkedIn.

23 Juli 12min

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