Conversational AI Quality & Failure Analysis

Overview

I review user conversations handled by a deployed AI voice agent and assess its behavior against the operating procedures that define its expected responses.

The focus is on decision-making: whether the agent understood the user’s intent, applied the appropriate procedure, and acted when it should. The goal is to identify recurring failure patterns that product and engineering teams can investigate.

My role

Quality analyst / evaluation specialist.

My work combines behavioral evaluation, intent and procedure-compliance analysis, failure categorization, cross-lingual interpretation, and structured reporting.

Behavioral evaluation

I assess the agent’s decisions rather than surface fluency alone, examining several kinds of failure:

• Intent misinterpretation: the agent misunderstands what the user is asking for.
• Knowledge gaps: the agent lacks information needed to respond appropriately.
• Under-triggering: an expected flow does not activate.
• Over-triggering: a flow activates without sufficient justification.

I distinguish agent-reasoning failures from upstream speech-recognition errors so the analysis identifies the appropriate source of the problem.

Structured, cross-lingual analysis

For each flagged case, I assign a category and write a concise, evidence-based account of the user’s request, the expected behavior, and the agent’s actual response.

Conversations and reports are in different languages, requiring faithful interpretation of user intent across languages. Consistent qualitative coding supports comparison and traceability across cases and procedures.

Purpose & use of the findings

Consistent categorization helps turn individual conversation failures into patterns: ambiguous procedures, repeatedly misread intents, and flows that activate too readily or fail to activate.

The reporting is designed to give product and engineering teams evidence they can use to investigate issues and prioritize improvements.

Skills applied

• Conversational AI and voice-agent evaluation
• Failure taxonomy design and structured qualitative coding
• Intent and policy-compliance analysis
• Cross-lingual analysis
• Clear, actionable reporting