Observability is undergoing its biggest transformation since the invention of distributed tracing. The field is moving from passive dashboards that humans must interpret toward autonomous AI agents that diagnose, recommend, and eventually remediate issues. In this talk, Costa Tsaousis traces the architectural patterns and machine learning (ML) foundations enabling this evolution.

Drawing on three years of building production AI observability systems, including an 18-model consensus engine achieving a theoretical false-positive rate of 10⁻³⁶ and a composable agent framework powering 22 specialized troubleshooting agents, the session explores:

• Why consensus-based ML beats threshold alerting: the mathematical case for 18-model ensembles in production
• Multi-agent orchestration patterns: when to use advisors, routers, and handoffs for complex troubleshooting
• The road to AI co-SRE: what is achievable today versus what is coming in 2026–2027.
• Practical implementation: how to avoid the pitfalls that derail most AI observability projects

Attendees will leave with a framework for evaluating AI observability tools and a realistic assessment of where the technology stands today.