ABOUT THIS EPISODE
In this episode, Itamar and Guy Fighel go deeper into the realities of building and maintaining AI systems at scale. Guy explains why data quality still outperforms clever algorithms and why reliability, trust, and observability are becoming the real bottlenecks in modern AI engineering.
The conversation challenges the idea that AI failures are mysterious. Guy breaks down how classic observability practices still matter and where a new layer is required, including dynamic monitoring, real-time feedback loops, and the idea of micro guardrails that guide agent behavior without blocking innovation.
He also discusses how AI is reshaping team structure and velocity. The future, in his view, belongs to smaller and stronger engineering groups with far better tooling and far higher expectations for iteration speed.
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