What boundaries should define our relationship with agentic AI in large-scale systems with Sam Newman

Episode notes

As agentic AI gets more advanced, how do we decide where its independence should start and stop? In this episode of Tech Tomorrow , David Elliman speaks with consultant and author Sam Newman about setting boundaries for agentic AI in large-scale systems. They also discuss why planning for uncertainty is now a key issue for many business leaders, and how doing small experiments with AI is ultimately the best approach. Sam points out that non-determinism in agentic AI is a major challenge because its results are not always predictable. When these AI workflows are connected, small mistakes early on can spread and impact later parts of the system. To handle this, Sam suggests breaking systems into smaller, manageable parts and adding checks between steps to catch problems early. He also highlights the importance of being able to trace issues and roll back changes, so teams can fix problems and recover from failures. These steps are only possible if boundaries are set early and humans stay in the loop throughout. They also talk about designing systems, so AI does not become a complicated dependency.

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