このエピソードについて
AI adoption stalls when licenses and quotas replace trust, context, and verification. Fernando Gonçalves and Vladimir Samoylov explain how DevOps makes agentic coding safe to scale through checkable starter tasks, organization-specific context, independent tests, Terraform plans, and deterministic gates—while exposing why token metrics, AI-written tests, and advisory Markdown can create false confidence.
Episode page (show notes and links): https://getboris.ai/insights/016-how-devops-makes-ai-safe-to-scale/
Episode page (show notes and links): https://getboris.ai/insights/016-how-devops-makes-ai-safe-to-scale/
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Loop engineering solves the coding-agent babysitting problem by giving DevOps teams a way to run larger tasks with evidence, constraints, and a clear definition of done. Andrey Devyatkin, Vladimir Samoylov, and Fernando Gonçalves unpack outer loops around agents, context-window limits, unattended r…
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