From Agent Traces to Assurance Interpretations: Behaviour- and Journey-Aware Evidence for Conversational Agents

Outcome scores can indicate that a conversational-agent task appears successful without explaining what occurred during execution or where a weakness arose. Raw runtime traces provide observations, but do not automatically constitute human-understandable assurance evidence; journey averages can also dilute weaknesses in configured critical stages. We present an implementation-grounded demonstrator that transforms selected runtime events into an assurance-oriented behaviour interpretation, control-oriented evidence, and provenance, alongside a separate journey-aware outcome lane. The deterministic demonstrator compares flat mean, minimum-turn, critical-stage-only, critical-stage gate, and journey-weighted outcomes with behaviour-only, journey-only, and unified assessments across 15 controlled scenarios. The scenarios show controlled contrasts in policy response, loop and routing behaviour, recovery, escalation, and critical-stage outcomes. Journey stages and scores are researcher-supplied fixture inputs, while interpretations are derived from implemented events, signals, controls, and evidence rules. No reported result uses a live model or external service; the demonstrator makes no production-scale, human-rated, statistical, benchmark, superiority, or generalisation claim.