Genesis Internal Tech Insights Day | How We Built AI-native SDLC
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Genesis Internal Tech Insights Day | Як ми побудували AI-native SDLC

Genesis
1:01:26
Jun 8, 2026
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Tech Insights Day — одноденна подія для розробників екосистеми Genesis та партнерських компаній, присвячена обміну досвідом щодо впровадження ШІ, трансформації ролей у командах та пошуку перевірених рішень у розробці.

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AI integration in development
SDLC automation
Managing technical debt
The role of engineers in AI processes
AI in testing and code reviews
TL;DR

Genesis explores AI integration in the SDLC process, focusing on automation and development efficiency.

8
Watch Score

Substantive content with valuable technical insights for developers.

1/10
Clickbait
positive
Sentiment
Should watch

Interesting for AI developers and those implementing automation in development.

Can skip

Not interesting for those who are not in development or not engaged with AI technologies.

Quality (8/10)

Complex technical material with practical examples of AI integration.

Summary
At the Genesis Internal Tech Insights Day, the integration of artificial intelligence into the Software Development Life Cycle (SDLC) process was discussed. The main focus was on automating and speeding up all stages of development, from drafting technical specifications to testing and releasing code. The company utilizes AI to generate technical specifications, write code, and conduct code reviews, significantly reducing the need for manual labor and allowing non-technical specialists to make changes to the product. Speaker Igor Zakutynskiy detailed the stages of integrating AI into the SDLC, emphasizing the importance of quality planning and an automated approach to testing and controlled acceleration of processes where appropriate. This approach allows developers to focus on architectural design and system interaction. The event also addressed issues of technical debt caused by AI-generated code and the necessity of managing it through automated processes and logging systems. Participants were interested in the role of developers in the new AI-driven process and the implications for the job market as AI changes the requirements for technical and product skills of specialists.
Agents and Their Functions6
  1. 1Discovery Agent — Forms a structured technical specification (TS).
  2. 2Design Agent — Generates design within the TS.
  3. 3QA Agent — Performs automated product testing.
  4. 4Code Review Agent — Analyzes code quality and identifies errors.
  5. 5Task Implementation Agent — Automates task execution on backlogs.
  6. 6Manual QA Agent — Emulates tester actions to identify bugs.
Key Takeaways
  • AI integration speeds up the SDLC.
  • Automation reduces the need for manual labor.
  • AI helps improve planning and testing.
  • Non-technical specialists can make changes.
  • The need for process logging is increasing.
  • The role of the developer focuses on architecture.
  • Technical debt requires separate management.
  • Tasks require clarity and planning.
  • Requirements for engineering thinking are increasing.
  • There are risks and dependencies on AI providers.
Action Items
  • 1Evaluate the possibilities of implementing AI in your own SDLC.
  • 2Consider automating testing and code reviews.
  • 3Familiarize yourself with AI services for integration in development.
Prerequisites
  • Knowledge of software development principles
  • Experience with AI technologies
Key Definitions
AI Native SDLC
A software development cycle where AI is a key element at all stages.
Content Analysis
Type

lecture

Sentiment

positive

Difficulty

advanced

Complexity

technical

Target Audience

Technical specialists, developers, AI engineers

#ai#development#integration#technical debt#code review#sdlc#automation#testing#programming#engineering#product approach#adoption#organizational changes