90% of AI prototypes never reach production (w/ Temporal’s Samar Abbas) | AI Basics

The gist

1. The problem is rarely the AI model itself: agents fail in production because they lack durability, failure recovery, and orchestration infrastructure.
2. Enterprises won't deploy agents without guardrails, audit trails, and the ability to intercept and validate AI-generated commands before they execute.
3. Full visibility into what tools an agent called and which LLMs it queried is essential for debugging hallucinations and trusting AI-driven processes.
4. Moving agents from a laptop to a distributed, team-scale environment is a distinct engineering step that requires properly deployed infrastructure, not just experimentation.