AI agents are everywhere in customer service, sales, and operations. But their ubiquity also begs the question: Are my AI agents doing a great job or frustrating customers at an even greater scale? The only answer is to do real AI agent evaluation – not basic resolution and analytics from the AI agent provider – but in depth analytics on which issues it is resolving successfully and where it’s falling short. Without measuring outcomes like accuracy, experience, and business impact, you miss opportunities to tune your AI Agents for the right use cases, with guardrails, and understand when to make smart handoffs. Skip that, and you risk frustrated customers, legal exposure, and wasted spend.
What “AI agent evaluation” really means
AI agent evaluation is the discipline of measuring how well an agent performs the tasks it’s intended to do. It’s not the same as classic software testing. You’re evaluating conversation quality, context handling, handoff timing, compliance to policies, and efficiency – and how all of that maps to outcomes like CSAT, cost per case, and revenue. The goal isn’t 100% automation; it’s the right automation, where AI excels and humans handle the rest.
Why this matters now more than ever
Two dynamics set the stakes. First, public examples show that poor evaluation can turn into brand and legal risk. Last year, an airline made big news when their AI agent hallucinated a policy that didn’t exist. A tribunal held that airline liable after its chatbot provided misleading information on bereavement fares; the decision made clear that companies are responsible for information on their sites, chatbot included. This is by no means the only example, but a clear one that AI agent evaluation and guardrails are absolutely necessary before scaling.
Second, leaders are scaling agents quickly. Sometimes to solve problems that their customers wish they wouldn’t solve. Salesforce recently replace their search capability with an AI agent, leading to customer frustration and major pushback. That’s why understanding which issues to solve – and which to leave to self-service or even human agents – is an important part of your customer service channel mix.
Forecasting Your AI-to-Human Agent Ratio
Understand where you should apply AI agents – and where human agents work best – to maximize your AI investment without sacrificing customer experience quality.
A practical framework for evaluating AI agents
1) Map conversation complexity
Start with a simple grid: Nature of the task (Transactional ↔ Consultative) and complexity (Simple ↔ Complex).
- Simple Transactional tasks (password resets, order status) have the highest automation potential.
- Simple Consultative tasks (product recommendations, basic troubleshooting) can work, but this is where many public failures happen when agents invent policies instead of retrieving ground-truth sources.
- Complex Transactional and Complex Consultative issues demand stricter guardrails and faster, cleaner handoffs to humans.
Check out the playbook above for this framework spelled out in more detail.
2) Prioritize use cases by conversation complexity
Rank your use cases using signals that actually correlate with risk and effort:
- Sentiment delta (sentiment for first message minus last message),
- Topic complexity (issue type, steps required),
- Exchange count (how long it takes to get to resolution).
This helps you avoid starting with the most complex, high-effort interactions and instead start on the ones that both you and the customer want to automate. This also helps you continue to spot new opportunities to automate over time, so your AI agent continues to tackle more and more new issues.
3) Measure performance with multi-dimensional metrics
In order to make sure your AI agent evaluation is actually producing the right results, you need to track four these four elements:
- Accuracy: intent categorization, resolution, policy compliance
- Efficiency: number of exchanges, AHT, cost per resolution, throughput
- Quality: CSAT and/or CQ (Conversation Quality), sentiment delta
- Business impact: revenue per conversation, upsell rate, churn indicators (e.g. frustration, confusion, etc.)
Analyze these factors by each intent/Contact Driver to you can pinpoint exactly where AI agents are succeeding and where they either need more training or to route to your human agent teams. You should also monitor these factors over time, to keep an eye on any changes in quality, volume, or user behavior that’s signal its time for retraining.
How Loris does evaluation (and why it works)
Loris is built for conversation analysis, turning chats, emails, and calls into structured data: Contact Drivers (why customers reached out), Sentiment and Sentiment Delta, and Conversation Quality (a predictive CSAT proxy). That layered system—multiple models working in concert—lets you measure, compare, and tune agent performance without relying on guesswork.
Customers use this to cut cost and protect experience. Calendly shaved a little over three minutes of AHT and reduced cost per case by 23% after instrumenting with Loris, then expanded to QA and Voice of the Customer to coach agents and fix upstream issues.
Best practices & how to get started
Start with baselines from human-handled conversations, then pilot on Simple Transactional use cases to prove value. Design explicit handoff rules when confidence is low, and evaluate the handoff itself – timing, context passed, and customer sentiment after transfer. Track conversation attributes, like exchange count, topics discussed, sentiment trajectory, alongside outcomes so you can see where agents help or hurt. And always balance objectives: higher automation is worthless if it drags down satisfaction or kills upsell opportunities. Sometimes a customer service interaction is the only chance you have to connect with your customers. Don’t automate away what could be your chance to stand out against competitive alternatives.
Pitfalls to avoid
- Optimizing for “accuracy” alone. You can be factually correct and still tank the experience if tone is off or effort is high. Don’t discount the empathy factor when it comes to specific topics or use cases. Sometimes we just need a human.
- Ignoring rare-but-critical scenarios. Test edge cases and escalations; many headline failures come from the “Simple Consultative” band where AI agents make a leap in judgement that leaves you exposed.
- Static frameworks. Customer behavior, policies, and models change. Your evaluation approach must adapt. Just because you AI agent is 100% accurate today, doesn’t mean it will be
What’s next in AI Agent Evaluation
AI is constantly changing. But as of this moment, the next phase seems to have multi-modal agents (voice + text), stronger context retention across sessions, and more formal “ethical and bias” checks. Customers, regulators, and even organizations themselves will expect clarity into why AI agents are making decisions, especially as the use cases and decisions they make are more nuanced. Having this evaluation layer gives you a clear record and understanding of where the boundaries are, as well as the confidence to build for these new use cases.
The bottom line
AI agent evaluation is the new competitive advantage. Teams that measure the right things can scale automation responsibly, route the hard stuff to humans, and keep customers happy while cutting costs. If you wait for your customers or the latest news story to tell you what’s wrong with your AI agent, it’s too late.
