Comment on: Metoro

@chrisbattarbee Generating telemetry at the kernel level with eBPF to remove the instrumentation overhead is a strong approach. That part makes a lot of sense, especially given how inconsistent telemetry can be across services and teams.The part that feels much harder is the auto-fix layer. In real systems, issues are rarely isolated. You often have partial signals, cascading failures, or symptoms that look like root causes. In those cases, even getting the diagnosis right is non-trivial, let alone generating a fix that is safe to apply.How do you validate that a generated PR is actually safe in production and not just technically correct in isolation? For example, avoiding cases where the fix resolves one symptom but introduces regressions elsewhere or conflicts with existing infra assumptions.I’ve been working in a similar space on the code side with Codoki.ai (AI code review and automated fixes), and even at that level, ensuring suggestions are reliable and not contextually wrong is a constant challenge, especially as systems get larger and more complex. So pushing this into infra-level auto-remediation is a big step.Would be interesting to understand how you’re handling validation, rollback strategies, or confidence scoring before applying fixes.Congrats on the launch.