Failure Surface Mapping

Failure Surface Mapping is the practice of enumerating every distinct way an LLM-powered workflow can fail before choosing evaluation tooling. Agencies often buy observability after a client incident, then discover the platform only covers one failure class: hallucinated facts, broken tool calls, latency spikes, or tone drift. Mapping the surface first turns tool selection into coverage math. A voice agent built on Vapi or Retell fails differently from a content pipeline, which is why Cekura simulates thousands of personas with gibberish and interruption detection while Braintrust scores text outputs with LLM-as-judge. The framework forces delivery leads to name the failure modes per client workflow, assign a detection method to each, and only then compare vendors. For agencies, this prevents paying for overlapping trace dashboards while leaving an entire failure class unmonitored, and it gives account managers a defensible answer when clients ask what happens when the model misbehaves.