O11ycast

On episode 93 of o11ycast, Ken Rimple and Martin Thwaites sit down with Mike Goldsmith to explore how teams can control telemetry costs without sacrificing the data they need to understand their systems. They discuss choosing the right signals, reducing repetitive logs, building more efficient OpenTelemetry Collector pipelines, and using dynamic sampling to preserve valuable traces while cutting unnecessary volume.

On episode 92 of o11ycast, Ray Myers joins Ken and Jess to explore how observability, reliability engineering, and formal software engineering practices are becoming even more important as AI coding agents take on larger roles in development. Rather than viewing AI as a replacement for established engineering disciplines, Ray argues that techniques like continuous delivery, testing, specifications, and formal verification provide the confidence needed to safely harness AI-generated code.

On episode 91 of o11ycast, Ken Rimple and Jess Kerr sit down with Janaki Vivrekar. Janaki shares how Amplitude is building AI-powered analytics agents, why evaluation frameworks are becoming essential to AI product development, and how teams can use observability techniques to improve agent performance over time. The conversation explores eval-driven development, production feedback loops, and the challenges of helping AI systems reason about complex business data.

On episode 90 of o11ycast, Ken Rimple and Jessica “Jess” Kerr speak with Cory Ondrejka. Together, they unpack the rise of agentic AI, the shifting identity of software engineers, and the growing importance of measuring real-world impact. Cory shares his concept of Outcome Engineering and how teams can adapt to a world where building is fast but validation is everything.Come find the Honeycomb team at O11yCon on May 21st!https://events.honeycomb.io/o11yConSF2026Use discount code SFCast2026 for 20% off

On episode 89 of o11ycast, Ken Rimple and Charity Majors are joined by Bryan Cantrill. They dive into the origins of observability, the realities behind AI productivity gains, and the tension between cloud convenience and infrastructure control. The discussion highlights how major tech shifts often look obvious only in hindsight.