Feb 9
Loading video player...
Yesterday, your checkout was fast; today, it’s slow. Nothing obvious changed, but latency spikes, timeouts, and retries are suddenly everywhere, and every minute costs real revenue. In this video, we walk through a real checkout performance incident in a complex microservices architecture and show how to find the one underlying cause behind many symptoms. What you’ll learn in this video: ► What end users actually experience during a checkout slowdown ► Why modern distributed systems produce many symptoms from a single root cause ► How to identify the first break in the chain, not just downstream noise ► Why dashboards, service maps, and alerts often fail to explain what actually broke ► How causal analysis separates cause vs effect in complex systems How this incident was diagnosed Instead of starting from metrics and drilling blindly, we start from causality. Causely automatically builds a causal graph of the incident, showing: ► How the problem propagated through dependent services ► Which signals are effects vs true causes ► The real blast radius of the failure ► Why certain services look broken even when they are not Metrics, logs, and traces become evidence, not noise. Within minutes, you get a clear explanation of: ► What happened ► Why it happened ► Where to act to fix it From incident to code-level fix We also show how Causely connects to your coding agent via MCP, tracing the issue back to the exact code change that caused the slowdown and pushing it directly into the engineering workflow. Who this video is for ► Backend and service engineers ► Platform and infrastructure teams ► SREs and on-call engineers ► Anyone troubleshooting latency, errors, or timeouts in microservices This is how teams can troubleshoot faster, reduce downtime, and prevent revenue loss in complex distributed systems.