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 unpacks why context, governance, and clear objectives mattered more than raw intelligence.

Rather than focusing on demos or theoretical promises, the discussion highlights a deeper shift that happened this year: a change in mindset. Engineers and decision-makers are no longer just asking whether something can be automated, but whether it should be allowed to decide at all.

The episode closes with practical, low-pressure recommendations for the holidays: how to learn about agentic AI without hype, why slowing down and revisiting systems thinking matters, and how small, safe experiments can teach more than ambitious prototypes.

A calm, slightly irreverent end-of-year debrief—designed to help listeners start the next year with fewer illusions and better architectures.