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Root cause analysis

Your AI Agent Is Confidently Wrong About Production — Willem Pienaar, Cleric

Your AI Agent Is Confidently Wrong About Production — Willem Pienaar, Cleric

Willem Pienaar, CTO of Cleric, details why AI agents struggle with production debugging compared to code generation. He explains the unique challenges of dynamic, unverified production environments and LLMs' inherent confidence bias. Pienaar outlines four common agent failure modes (symptom/cause confusion, missing baselines, abstraction loss, anchoring) and introduces grounding techniques—like generating multiple theories, using baselines and dependency graphs, temporal analysis, and evidence propagation—to enhance diagnostic accuracy. He also emphasizes the critical need for verifying fixes against real outcomes to calibrate agent confidence, ultimately aiming to bring production signals into the development environment for proactive failure prediction.

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

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.

What it takes to be a top PM today | Robby Stein (Google Search)

What it takes to be a top PM today | Robby Stein (Google Search)

Robby Stein, drawing on his work on Instagram Stories, Reels, and AI-era Google Search, argues that the core craft of product management is making great decisions. He lays out a three-part process: understand people's underlying needs through jobs to be done, diagnose why a product is failing through root cause analysis and iteration, and refine the craft details that make a product work well and feel delightful.

Learned Execution Graphs for Anomaly Detection & Drift in APIs — Ritvik Pandya, JP Morgan Chase

Learned Execution Graphs for Anomaly Detection & Drift in APIs — Ritvik Pandya, JP Morgan Chase

Ritvik Pandya from JP Morgan introduces 'learn execution graphs'—short-lived DAGs representing API request processing—to detect anomalies and drift in high-throughput systems. This approach localizes performance issues to exact nodes, identifies skipped or reordered steps, and differentiates between transient anomalies and fundamental system drift (structural, volume, or behavioral). It leverages per-client baselines and OpenTelemetry data to reduce mean time to discovery (MTTD) from hours to seconds, enhancing system reliability and observability.

AI Agents: Transforming Anomaly Detection & Resolution

AI Agents: Transforming Anomaly Detection & Resolution

Martin Keen explores how agentic AI can significantly reduce IT downtime and Mean Time To Repair (MTTR) by moving beyond naive data dumps and embracing context-aware analysis. The key lies in using topology-aware correlation to curate relevant data for an AI agent, which can then systematically identify the root cause, provide explainable insights, and generate actionable remediation steps, ultimately augmenting human SREs rather than replacing them.

From DevOps ‘Heart Attacks’ to AI-Powered Diagnostics With Traversal’s AI Agents

From DevOps ‘Heart Attacks’ to AI-Powered Diagnostics With Traversal’s AI Agents

Anish Agarwal and Raj Agrawal, co-founders of Traversal, discuss how their AI agents automate root cause analysis (RCA) for critical system failures. They detail their agent's architecture, which leverages causal inference and large-scale computation to systematically find the root cause in minutes, and argue that the rise of AI-generated code makes AI-powered debugging an essential capability for modern software engineering.