The pitch for autoremediation is hard to resist: AI doesn't just surface issues faster — it fixes them on the spot, leaving you to kick back, validate, and observe. MTTR doesn't just shrink; it becomes a relic. Problems vanish before anyone even notices they existed.
But rush into it without solid data, proper curation, and clear policy, and you're pulling a tap with too much pressure — nothing but foam, no beer.
Closed-loop remediation isn't a shortcut. It's the payoff at the end of a disciplined, AI-driven observability practice.
In this talk, we'll walk through the three things that make autoremediation actually work:
- System coverage that holds up at real scale
- Data that's clean, navigable, and actionable
- Ground rules for what AI is — and isn't — allowed to do
You'll walk away with a practical readiness checklist and a clear framework for deciding where autoremediation belongs in your stack, and where it definitely doesn't.
No hype. Just the work that earns AI the right to act in production.
