Your business metrics are dropping. Nobody knows the cause.

Your agents can see symptoms everywhere. They can't compute what caused the business impact.

A business metric drops. Cart abandonment spikes. Bet acceptance rate falls. Ad campaign delivery stalls.

The problem is that no tool your agents can query knows that a 200ms degradation in your pricing service caused cart abandonment to spike 15%. Correlation can tell you two things happened at the same time. It can't tell you one caused the other. That distinction, between symptom and cause, is what determines whether your engineers spend 90 minutes in a war room or 90 seconds reading a causal query result.

So engineers start from scratch. Pull the business metric. Hypothesize which services touch that flow. Query each one. Try to establish correlation manually. It takes 60–90 minutes before a hypothesis. By then the business has already escalated.

Your observability stack tells you what's degraded. It doesn't tell you what that degradation caused.

Causely computes the causal path from business impact back to infrastructure root cause.

Business metrics are mapped to the service dependencies that drive them. When a metric degrades, Causely traverses the causal graph and returns the infrastructure root cause with a deterministic path. The output is a cause, not a list of correlated signals or a dashboard to interpret.

This matters most when the business asks why and engineering needs an answer in minutes, not hours.

If your business can calculate what a minute of checkout degradation costs, or what a drop in bet acceptance rate means in revenue per hour, that number should be part of how your agents and engineers prioritize and escalate.

Causely doesn't invent that relationship. It encodes the causal knowledge your senior engineers carry and makes it computable, so every agent has it, every time.