Amazon AGI director: AI agent reliability, not raw power, blocks enterprise deployment

The enterprise AI sector faces a paradox: broad experimentation without production traction. Cisco's finding that 85% of enterprises pilot AI agents but only 5% deploy them to production reflects a critical disconnect between lab performance and real-world reliability. Silverthorn's framing addresses this head-on by arguing that the problem is not benchmarking sophistication—it is that traditional evaluations measure narrow capability (how well an agent solves a test problem) without measuring the four dimensions that actually determine whether an agent will work reliably when a customer depends on it. His four-part framework—consistency (does it behave the same way each time?), robustness (does it handle edge cases?), predictability (can you anticipate its failures?), and safety (does it avoid causing harm?)—reorients the conversation from "how smart is it?" to "can I trust it?" This distinction is significant because it implies that vendors and enterprises may be measuring success on the wrong metrics, explaining why agents pass internal evals and then "collapse in the wild." Silverthorn's background—he came to Amazon through the acquisition of Adept AI, a startup that built autonomous AI agents—suggests Amazon is embedding this thinking directly into its AGI development strategy.