Re:Ask HN: How are you monitoring AI agents in production?
by RovaAI on Tuesday March 10, 2026 @06:24AM
(#47321343)
devonkelley's dashcam framing is right. The useful question isn't "how do I see what happened" - it's "how do I catch irreversible actions before they happen."
The failure modes from those incidents aren't really observability gaps. They're about permission scope and action reversibility. An agent deleting a database doesn't need better logging after the fact - it needs a clear model of what's reversible and what isn't, built into the execution loop.
What works: classify every action as either local/reversible (reads, file edits, drafts) or external/irreversible (sends, deletes, pushes, payments). The former runs autonomously. The latter gets a confirmation checkpoint with no exceptions. That one split eliminates most incident surface area without needing a dedicated SDK.
Langfuse/LangSmith are useful for cost tracking and debugging post-hoc. But they're tools for the team, not the agent. The reversibility model needs to be at the framework level.
The failure modes from those incidents aren't really observability gaps. They're about permission scope and action reversibility. An agent deleting a database doesn't need better logging after the fact - it needs a clear model of what's reversible and what isn't, built into the execution loop.
What works: classify every action as either local/reversible (reads, file edits, drafts) or external/irreversible (sends, deletes, pushes, payments). The former runs autonomously. The latter gets a confirmation checkpoint with no exceptions. That one split eliminates most incident surface area without needing a dedicated SDK.
Langfuse/LangSmith are useful for cost tracking and debugging post-hoc. But they're tools for the team, not the agent. The reversibility model needs to be at the framework level.