Site reliability engineering

Oct 06, 2026

The 6 Pillars of an Agentic Harness for Production — Varun Krovvidi, Resolve AI

Varun Krovvidi of Resolve AI explains why AI excels at code generation but struggles with the complex, multi-domain problem of running and fixing software in production. He outlines five common failure modes for AI agents in production (anchoring bias, context issues, coherent vs. causal answers, guardrails, lack of learning) and introduces Resolve AI's six-pillar architecture: model orchestration, context engineering, causal reasoning, governed actions, learning systems, and multi-level evaluations. A demo illustrates how Resolve AI diagnoses a Grafana alert to its root cause, ruling out coincidental events and maintaining its causal reasoning.

Oct 02, 2026

The 5 Levels of Self-Driving Production — Eric Schwartz, Traversal

Eric Schwartz, a product manager at Traversal, argues that AI-generated code has moved engineering time from building to troubleshooting, and that observability dashboards show what broke but not why. He frames root cause analysis as a causal problem, lays out five levels of self-driving production, walks through alert intelligence at PepsiCo and incident response at American Express, and offers five questions for evaluating an AI SRE.

Apr 22, 2026

Beyond Line Charts • Yao Yue • YOW! 2025

Yao Yue challenges the dominance of line charts in telemetry visualization, arguing that their time-centric nature and misleading interpolations often obscure insights. She proposes a 'form follows function' approach, advocating for visualizations tailored to the data's shape, the metric's type (e.g., step charts for counters, heatmaps for histograms), and the specific, often 'timeless,' operational questions being asked. The key is to transform time-series data into more flexible formats like panel data to derive clearer, more direct answers.