Handling AI-Generated Code: Challenges & Best Practices • Roman Zhukov & Damian Brady

Feb 10, 2026

Damian Brady, Staff Developer Advocate at GitHub, and Roman Zhukov, Principal Architect at Red Hat, debate AI-assisted development. They cover how AI changes workflows, provenance and traceability of generated code, security and supply-chain risks, legal and ethical responsibilities, and how developer roles and practices must adapt to keep humans in the loop.

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INSIGHT

AI Amplifies Workflows But Doesn't Replace Humans

AI tools act as amplifiers that speed simple developer tasks but can slow complex releases.
Humans remain essential for final product quality, testing, and release decisions.

ADVICE

Declare And Document AI-Assisted Code

Disclose AI assistance and document AI-generated contributions when committing code.
Developers remain legally responsible for code quality, security, and licensing.

ADVICE

Require PRs For Agent-Produced Code

Route agent-produced code through pull requests with traceability and human review.
Ensure agents submit PRs and record who requested and reviewed the changes.

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This interview was recorded for GOTO Unscripted.
https://gotopia.tech

Check out more here:
https://gotopia.tech/articles/419

Roman Zhukov - Principal Architect - Security Communities Lead at Red Hat
Damian Brady - Staff Developer Advocate at GitHub

RESOURCES
Roman
https://github.com/rozhukov
https://www.linkedin.com/in/rozhukov

Damian
https://bsky.app/profile/damovisa.me
https://hachyderm.io/@damovisa
https://x.com/damovisa
https://github.com/Damovisa
https://www.linkedin.com/in/damianbrady
https://damianbrady.com.au

Links
https://www.redhat.com/en/blog/ai-assisted-development-and-open-source-navigating-legal-issues

DESCRIPTION
Roman Zhukov (Red Hat) and Damian Brady (GitHub) explore the evolving landscape of AI-assisted software development. They discuss how AI tools are transforming developer workflows, making developers about 20% faster on simple tasks while being 19% slower on complex ones.

The conversation covers critical topics including code quality and trust, security concerns with AI-generated code, the importance of education and best practices, and how developer roles are shifting from syntax experts to system architects. Both experts emphasize that AI tools serve as amplifiers rather than replacements, with humans remaining essential in the loop for quality, security, and licensing compliance.

RECOMMENDED BOOKS
Phil Winder • Reinforcement Learning • https://amzn.to/3t1S1VZ
Alex Castrounis • AI for People and Business • https://amzn.to/3NYKKTo
Holden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine Learning • https://amzn.to/3JVngcx
Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) • https://amzn.to/3AQmIRg
Lakshmanan, Robinson & Munn • Machine Learning Design Patterns • https://amzn.to/2ZD7t0x
Lakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision • https://amzn.to/3m9HNjP

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