How to evaluate AI agents

As AI agents become central to customer experience strategies, leaders are bombarded with demos that promise the same results: better efficiency, lower costs, and happier customers.

But with the growing number of vendors offering voice and chat AI solutions, evaluating them effectively can be overwhelming. Demos often showcase best-case scenarios, yet what truly matters is how these systems perform in real-world conditions with your customers, systems, and service expectations.

Use this blog as your guide to confidently navigate product demos. Learn how to spot genuine solutions, evaluate vendors thoroughly, and ask the right questions. With key evaluation criteria and common pitfalls to avoid, you’ll be empowered to make informed, impact-driven decisions about the partners you choose to work with.

Can it understand you?

This is obviously the most basic requirement, but can the AI agent understand you? Can it solve for the never-ending “press 1 for billing” followed by “I didn’t quite catch that”? Most demos will showcase the AI agent engaging with a customer under “perfect” conditions, but real life is a little more complicated than that. Can the agent achieve the same level of success with the following:

1. Background noise
2. Overlapping speech
3. Strong accents or different languages

If a vendor is unable to provide examples of this, it’s a red flag.

Of course, this is only appropriate for voice demos and not all providers lead with voice, although it is still the preferred channel of choice for customer service inquiries. Webchat is another popular customer channel and quite common to use in demos because it’s easier to build as the AI agent already has the transcription it needs to understand a question or intent. If a vendor can only provide chat demos, it could be a sign that their Automatic Speech Recognition (ASR) capabilities are sub-par and they’re unable to provide the same experience quality over voice.

Does it sound natural?

Understanding your customers is only half the battle; how the AI agent responds will make or break the conversation (literally). Listen closely to how natural the agent sounds: is the pitch, pacing, and tone varied enough to convey emotion, or is the voice monotone and robotic? The agent should mimic human conversation, adjusting for urgency, topic changes, or emotional shifts.

Pronunciation is equally crucial, especially for names, addresses, technical terms, or jargon. A mispronounced surname or city can undermine credibility. Be sure to ask if the voice is customizable and if it can be updated to reflect your brand’s tone. Test various types of conversations, including emotionally sensitive ones, to observe how well the voice handles nuanced human interactions.

How well does it respond?

Response time isn’t just about how fast the system replies, but how naturally it responds within the flow of a conversation. The ideal AI agent has the perfect blend of latency and listening. That is, it responds promptly enough to show it’s listening, but also with a cadence that feels thoughtful, not robotic. Look for “thinking time” usage where the AI pauses slightly before delivering complex information, mimicking human reflection. This pause should feel intentional, not like a system delay.

Match pace to the moment

The system should also manage dynamic pacing, speaking slowly and clearly for long instructional content while keeping responses brisk for routine exchanges. Pay attention to the transitions between turns: are there awkward silences, overlapping speech, or jarring shifts? These micro-moments can be immersion-breaking and indicate the maturity of the conversation engine.

Handle multi-topic interactions

It’s also important to remember that real conversations aren’t rigid or linear. Customers interrupt, change topics, and go off-script. A robust agent must handle these dynamics gracefully. If a customer interjects or changes direction mid-sentence, the agent should pause and listen, then recover contextually. Avoid systems that dominate the conversation or prevent natural dialogue with fixed scripts—this “railroading” limits customer interaction and creates frustration. Test multi-topic interactions, like checking a bill and making a return, all in one call. These behaviors distinguish a conversational agent from a glorified IVR.

Does it complete the correct action?

Understanding and responding are table stakes, but if an AI agent can’t complete the required action, then it’s a waste of time. If you ask to track a package and provide the order number, can the AI agent respond? If it does, do you receive the correct answer, or does it make one up? Does it just gather data, or can it act on it? Assess whether it supports both reactive use cases, such as inbound customer service requests, and proactive ones like appointment reminders or upsell campaigns.

It’s also crucial to remember that, due to the nature of some calls or customer preferences, some calls will need to be escalated by AI agents to humans. You’ll want to ask your vendor:

• How well does the AI agent handle handoffs to human agents?
• When a call is transferred, does it send context and transcript seamlessly?

Even if an AI agent doesn’t automate 100% of your calls, it still plays a vital role in improving the customer and agent experience when an interaction has to be escalated.

How well does it work in the real world?

Any vendor can pull together an impressive demo that isn’t real-world tested. Actual customer interaction is where true value is measured. Ask vendors for examples of their agent in production environments and call into a live line that uses the technology. Put yourself in the shoes of your customer interacting with this system and use the techniques discussed above to observe whether the experience remains strong when faced with real-world unpredictability. If a vendor is unable to supply this, or the experience is worse than the demo you watched, take it as a significant red flag.

In addition to the conversational experience, be sure to review which systems the agent can integrate with, what technical lift is required, and whether it supports your telephony provider or tech stack.

It’s also important to clarify compliance readiness for any applicable standards, particularly in highly regulated industries like healthcare and banking. Look for AI agents with ISO27001, SOC-2 Type 2, Cyber Essentials, PCI and HIPAA certifications. If an application is unable to meet these requirements, it is much more a science experiment than a solution ready for the robust needs of an enterprise.

Some questions to consider:

• What backend systems can the agent connect, and are those integrations native or do they require custom APIs?
• Does the vendor provide resources to assist with onboarding, or are you left on your own to deploy? Investigate how deeply the AI integrates with your core platforms (CRM, ticketing, billing, etc.).

How is the application maintained?

Sustainable AI success relies on active management. Ask the vendor who owns the training and ongoing optimization of the system. Is it your internal team, the vendor’s ops team, or a hybrid model? The vendor should offer clear tooling for monitoring live interactions, reviewing edge cases, and updating flows or FAQs without needing to retrain models from scratch. Look for version control, change tracking, and rollback capabilities to ensure updates are able to be properly tested before being rolled out to customers.

Also, inquire how frequently the system learns from new data: is there a retraining schedule? Make sure there are robust platform capabilities available that allow you to easily manage and edit the application to reflect seasonal trends or emerging customer behaviors. The system should also have enterprise ready analytics capabilities that let your team track KPIs like containment, CSAT, deflection, and sentiment. Further still, the data should be rich enough to help you make decisions that positively impact areas of the business outside of the contact center.

Ultimately, the system must be built for continuous learning and operational agility. If improvement is slow or opaque, long-term value will suffer even if the launch is strong.

How to choose the right vendor for your needs

Selecting the right AI agent is not just about ticking boxes or being wowed by a polished demo. It’s about ensuring the solution can handle the full complexity of your customer interactions, integrate seamlessly with your systems, and evolve over time. By focusing on the questions in this blog, from listening accuracy to deployment operations, you’ll gain a clear framework for meaningful comparison. Ask tough questions, demand real-world examples, and prioritize vendors who offer transparency and long-term partnership.

With careful evaluation, you’ll be positioned to choose an AI solution that doesn’t just meet expectations in a demo, but delivers measurable business value from day one.

Want to know more about how PolyAI creates AI agents that improve CX? Request a demo today.