Conversation Analyzer: Automated Quality Evaluation for Enterprise AI Agents

Every conversation your AI Agent handles is a source of insight. What topics are customers bringing that you haven't designed for yet? Where is your AI excelling, and where is there room to close the gap? 

By applying LLM-based qualitative analysis to every conversation, Conversation Analyzer opens a new layer of quality intelligence, transforming transcript history into a continuous, structured view of customer needs, agent performance, and opportunities to improve service.

The Quality Gap in AI Agent Performance Metrics

Traditional conversation analytics primarily surfaces what is easy to quantify. Conversation traffic, containment rates, channel-specific sessions, and flow completion metrics are all valuable for outcome-based reporting. But they only tell half the story.

Measuring conversational quality has never fit neatly into a metric formula – even more so with Agentic AI, where every response varies from one conversation to another. Did the AI show empathy? Did it follow its instructions correctly? Did it handle sensitive data appropriately? Did it provide consistent answers across similar conversations?

These behaviors matter enormously to customers, compliance teams, and your overall customer service strategy. But they can't be captured by session counts or flow completion flags. As a result, teams have historically fallen back on manual reviews that sample a fraction of transcripts and often react only after issues have affected enough customers to become visible. As AI Agent deployments scale, this gap compounds.

Automated Evaluation of AI Agent Quality, Across Every Conversation

Conversation Analyzer is a new quality evaluation tool that changes the fundamental premise of how enterprises measure AI Agent performance.

It applies LLM-based qualitative judgment retroactively to real production conversations, assessing nuanced behaviors like empathy, instruction-following, and regulatory disclosure accuracy that traditional metrics simply cannot capture. And it does this across every conversation, not a sample.

Teams define what quality means for their business. Analyzer runs on your conversations on a schedule or on demand, and surfaces results across three dashboards.

Topic Discovery: Spot Demand Patterns with Data Clustering

Quality scores are most meaningful when viewed in context. If customer conversations suddenly shift toward a new issue or product, declining quality may reflect an emerging customer need rather than a regression in AI behavior.

Topic Discovery provides that context by automatically identifying the topics and subtopics customers are discussing across every conversation -without any manual tagging or pre-configuration.

Rising issues can be surfaced before teams would typically detect the pattern manually. For example, if conversations about fraud alerts surge overnight, the Topic Discovery dashboard flags that spike immediately, prompting timely investigation before it escalates into a broader incident.

As your conversation landscape evolves, AI-powered topic suggestions help you continuously uncover new themes. After a banking product launch, for instance, you might find hundreds of conversations appearing as newly detected topics, revealing that customers are asking about the new premier savings account.

Quality Evaluation: From Built-In Criteria to Enterprise-Specific Requirements

Once you understand what customers are asking about, the next question is whether your AI handled those conversations effectively. LLM-based analysis evaluates nuanced behaviors that have historically been difficult to measure consistently at scale: from how well your AI follows instructions to whether it communicates with the right level of empathy.

Conversation Analyzer scores every conversation across four predefined criteria sets that cover the core dimensions of AI Agent performance, including:

• Customer Sentiment: overall sentiment, how it trends over time, and escalation risk
• Containment & Success: resolution rate, escalation frequency, and the specific reasons customers are being handed off
• AI Behavior Quality: how well the AI followed its instructions and used tools correctly
• AI Agent Experience Quality: conversational quality across politeness, empathy, professional tone, and response clarity
  

Beyond default criteria, Custom Evaluation lets you define your own scoring logic in plain language. This is valuable for compliance monitoring, as well as any business or industry-specific behavior. Examples include:

• Did the AI include all required disclosures and handle sensitive data correctly?
• Did it stay within its permitted scope and avoid providing legal, pricing, or medical advice?
• When a customer expressed intent to cancel or switch providers, how effectively did the AI respond?
• How consistently did it reflect the defined tone and voice guidelines throughout the conversation?

You can choose from binary pass/fail checks, 3- or 5-point scales, percentage scores, or numeric ratings, and the LLM evaluates each criterion against the full conversation transcript. Up to ten custom criteria can run alongside the predefined set, giving your QA, compliance, or operations teams an evaluation layer they fully own.

A Complete View of AI Agent Performance

Conversation Analyzer is a powerful addition to your existing outcome metrics, delivering a complete picture of your AI-powered customer service quality. For enterprise teams, this creates a shared quality layer across CX, operations, and governance teams.

• Quality and CX managers who need consistent conversation review without growing headcount. Every conversation gets evaluated against the same criteria with every run, removing the bias and inconsistency that comes with manual, limited sampling
• AI operations teams responsible for maintaining and improving performance after every deployment. Scheduled analysis surfaces declining sentiment trends, rising escalation risk, or drops in instruction-following before they appear in support volume or CSAT scores
• Compliance and risk teams who need documented, auditable evidence of AI behavior. Non-technical stakeholders can define and own quality benchmarks directly, without relying on engineering to instrument new metrics

Make AI Quality Continuous, Measurable, and Actionable

As AI Agents take on a larger share of customer interactions, the stakes for getting quality right have never been higher. Any miscommunication, missed disclosure, or failure to de-escalate a frustrated customer isn't just a service issue. It poses a compliance risk, a brand risk, and a signal that something in your deployment needs attention.

Conversation Analyzer gives you the visibility to act on that signal. By bringing LLM-based qualitative evaluation to every conversation, and making those results continuously accessible in purpose-built dashboards, it closes the gap between what traditional metrics report and what truly determines the quality of your customer experience.

Visit Conversation Analyzer documentation to learn more.