In this Christmas episode of the AI-Shed podcast, Alessandro and Giovanni step away from predictions and buzzwords to do what engineers actually do at the end of the year: a systems review. They look back at what really worked in AI over the past year, where expectations clearly outran reality, and why many so-called “agentic” systems failed in very predictable ways. From overconfident automation disguised as autonomy to agents that optimized themselves straight into chaos, the episode unpack...
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In this Christmas episode of the AI-Shed podcast, Alessandro and Giovanni step away from predictions and buzzwords to do what engineers actually do at the end of the year: a systems review. They look back at what really worked in AI over the past year, where expectations clearly outran reality, and why many so-called “agentic” systems failed in very predictable ways. From overconfident automation disguised as autonomy to agents that optimized themselves straight into chaos, the episode unpack...
In this episode, Alessandro and Giovanni explore why explainability is no longer enough for modern AI and agentic systems—and why observability must take center stage. They explain that explainability is retrospective (“why the system thinks it acted”), whereas observability provides real-time insight into what the system is doing, whether it remains within its safety envelope, and how far it is from violating constraints. Drawing parallels with fly-by-wire and safety-critical software, they ...
AiShed
In this Christmas episode of the AI-Shed podcast, Alessandro and Giovanni step away from predictions and buzzwords to do what engineers actually do at the end of the year: a systems review. They look back at what really worked in AI over the past year, where expectations clearly outran reality, and why many so-called “agentic” systems failed in very predictable ways. From overconfident automation disguised as autonomy to agents that optimized themselves straight into chaos, the episode unpack...
