Really liked the distinction between "is the system up?" and "is the system right?" That's the gap we've been seeing too. One thing I'd add is that observability tells you what happened, but production systems also need a runtime layer that can stop obviously bad actions before they execute. Feels like both are becoming essential as agents move beyond demos
Absolutely. That's one of the reasons we're building an AI observability platform. As we worked with production AI systems, we realized traditional metrics don't tell you whether an agent made the right decision. You need visibility into reasoning, context, tool execution, and outcomes to debug issues before they impact users.
Cool. We're working on a similar space with Failproof AI, but from the runtime reliability side. It's open source, and we've found that observability and runtime guardrails complement each other really well in production. Please checkout our open source repo of Failproof AI and make the star if you like it :)
Mayank Bansal
Really liked the distinction between "is the system up?" and "is the system right?" That's the gap we've been seeing too. One thing I'd add is that observability tells you what happened, but production systems also need a runtime layer that can stop obviously bad actions before they execute. Feels like both are becoming essential as agents move beyond demos