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Most CI/CD pipelines are built to detect failure, not to resolve it. As software systems grow more distributed and complex, that limitation is becoming a bottleneck for resilience.
In this episode, David sits down with Tomer Karin, a seasoned software architect in the automotive industry, to explore a new paradigm he calls Continuous Integration and Continuous Solution (CI/CS). Tomer argues that the future of automated software development isn’t just faster feedback loops, it's systems that can autonomously remediate failures using AI.
Tomer shares how his years of working on large-scale automotive software revealed the inefficiencies of traditional development pipelines, where engineers spend significant time diagnosing and fixing issues instead of building new capabilities. With CI/CS, AI agents don’t just identify failing builds, they apply fixes that are verified through existing test pipelines, allowing teams to start the next day with a healthier codebase.
Join us as we discuss:
In this episode, David sits down with Tomer Karin, a seasoned software architect in the automotive industry, to explore a new paradigm he calls Continuous Integration and Continuous Solution (CI/CS). Tomer argues that the future of automated software development isn’t just faster feedback loops, it's systems that can autonomously remediate failures using AI.
Tomer shares how his years of working on large-scale automotive software revealed the inefficiencies of traditional development pipelines, where engineers spend significant time diagnosing and fixing issues instead of building new capabilities. With CI/CS, AI agents don’t just identify failing builds, they apply fixes that are verified through existing test pipelines, allowing teams to start the next day with a healthier codebase.
Join us as we discuss:
- Why traditional CI/CD breaks down as systems scale and complexity increases
- What Continuous Integration and Continuous Solution looks like in practice
- How AI agents can safely diagnose and fix software failures
- Where human oversight fits into autonomous development pipelines
- The economic and productivity impact of autonomous software workflows
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