Episode 7: Evaluation, observability, safety, platforms, and future directions
The seventh episode of our Agentic AI and agentic workflows series, on what it takes to move agents out of the lab and into production — how they are measured, monitored, and governed once live, and the safeguards and operational discipline that keep them aligned, auditable, and resilient over time.
In this episode we explore what it really takes to move AI agents out of the lab and into the real world. We center the conversation on turning promising agent designs into reliable, production-ready systems that can operate at scale. Rather than focusing on how agents are built, we look at how they are measured, monitored, and governed once they are live.
We walk through the mindset and practices required to keep AI behavior predictable and trustworthy over time, from validating performance in live environments to maintaining visibility into complex agent behavior. We also highlight the importance of safeguards and operational discipline to ensure systems remain aligned, auditable, and resilient as they evolve. This episode is for anyone interested in bridging the gap between experimental AI and dependable, real-world applications.