In this episode, we explore what happens when AI leaves the lab and enters real-world production. We examine why most AI projects fail at deployment, how production systems differ fundamentally from research models, and what it takes to operate large language models reliably at scale.

The discussion focuses on the engineering, organizational, and governance challenges of deploying probabilistic systems, along with the emerging architectures that turn LLMs into agents capable of planning, tool use, and autonomous action.

This episode covers:

• Why most AI projects fail in production
• Research vs. production AI: reliability, consistency, and scale
• Build vs. buy trade-offs for LLMs
• Hidden costs: prompt drift, prompt engineering, and inference
• Evaluation, monitoring, and governance in real systems ...