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You've built an AI that identifies root causes of incidents faster than any human could... but there's one problem, no one trusts it.
Ops teams are skeptical by nature. They've been burned by noisy alerts, black-box tools, and "intelligent" systems that weren't. This talk covers what we learned building AI for incident response across enterprise environments: why technically correct recommendations get ignored, and how to design for skepticism from day one.
I'll share specific patterns that moved the needle:
- Validating agent responses before they reach users, catching hallucinations, weak reasoning, and overconfident outputs - Explainability that fits the operator's mental model, not the data scientist's - Feedback loops that improve the AI and build user trust simultaneously - Rollout strategies that let teams build confidence gradually
Whether you're using LLMs, agents, or traditional ML for operational tasks, the trust problem is the same. Ship something wrong during an incident and you've lost your users for months.
You'll leave with a practical framework for validating AI outputs and building the kind of trust that gets recommendations acted on.
Robert is Lead Enterprise Solutions Engineer at Chronosphere and an OpenTelemetry contributor. He recently presented on AI Observability with OpenTelemetry at Cloud Native London https://www.youtube.com/live/qF4wz-pha1w?si=PFzjNcGkbD4pFKnA&t=625 and has spoken at AWS Summit, and other... Read More →
