Santiago de Buen and Stefani Barbero

The adoption of autonomous AI agents is steadily increasing in contact centers, where they offer customers quicker service 24/7 and keep human agents’ queues manageable. How well each solution delivers depends on two things: what the provider prioritizes in the customer experience and how it uses generative AI to power its autonomous agents.

Providing an excellent customer experience consistently is a balancing act of technology, humanity, and efficiency. Customers want reliable responses and resolutions they can trust. At the same time, they want to avoid rigid experiences that don’t adapt to the realities of human conversation and real-world customer service. And let’s not forget speed and convenience. 

Every AI solution provider balances these customer expectations in its own way. But the current crop of AI agents tend to fall into three categories, and I would argue that only one of them is truly an autonomous AI agent. The other two fall short, each in their own way.

Category #1: The better bot

These solutions prioritize consistency and safety, but lack flexibility and do not take advantage of generative AI’s ability to plan and problem-solve.

Like traditional bots, these solutions rely on deterministic flows rather than leveraging generative AI’s ability to reason its way through the interaction. In other words, they run on rails and cannot deviate from the pre-determined paths. They can use retrieval augmented generation (RAG) to gather the information they need to craft a response. But the use of large language models (LLMs) is limited in these solutions. They typically use LLMs only to understand the customer, determine intent, and choose the deterministic flow that best fits the customer’s needs.

Here’s a typical example of how this solution breaks down in a customer conversation. This is an excerpt from an actual interaction in which the caller is trying to schedule a dental appointment.

‍

Despite the fluid conversation, the overall experience is rigid. When a customer switches topics or the interaction otherwise deviates from the planned conversation flows, the solution has a hard time adapting. That often leads to dead ends and a lack of resolution for the customer.

Overall, it feels like talking to a bot. A better bot, yes. But still a bot. 

Category #2: Flexible with everything, including the facts

Solutions in this category prioritize flexibility and fluid conversation. That combination can make them feel more human. In a demo, they shine. But without sufficient grounding and safety measures, the open-ended nature of the AI leads to misinformation. 

These solutions rely on the reasoning capabilities of LLMs. But instead of seeing their output as an ingredient that needs to be combined with other technologies to maintain safety and reliability, they treat the LLM’s output as the final product. That leads to a more natural feeling conversational flow. Unfortunately, dealing with an AI solution that lacks guardrails is a little like dealing with a pathological liar. Sometimes, it makes things up – and it’s hard to tell when it’s doing that.

Here’s a typical example of how this type of solution breaks down in a customer conversation. As with the previous example, a patient is trying to schedule a dental appointment.

And here’s the catch – there’s no one named Dr. Harris at this practice.

‍

The conversation flowed well, but the solution just scheduled an appointment with a dentist who doesn’t exist. And to make matters worse, it seemed to suggest that the caller could expect to have a non-existent discount applied. 

These types of solutions are inconsistent in their responses. Sometimes they’re accurate, and other times they’re misleading. And if you call again with the same questions, you just might get a different result. And you won’t necessarily know what’s true. 

Category #3: A solution that lives up to the name AI agent

This last category combines the safety and accuracy of the “better bot” with the open-ended nature of the solutions that prioritize flexibility. The result is a richer, more accurate, and more satisfying customer experience.

These types of agentic solutions leverage the full capabilities of LLMs to engage in free-flowing conversations, determine customers’ needs, and take action to resolve their issues on the fly. They use multiple models to plan, reason, take action, and check output for quality and safety. In these solutions, the output of the LLMs is an ingredient, not the final product. In addition to the LLM, these solutions incorporate a robust set of safety mechanisms to keep the AI agent on track, within scope, and grounded in your designated sources of truth. These mechanisms catch potential safety and security issues in the caller’s inputs, and prevent inaccurate information from being shared in a response. When this type of AI agent does not know the correct answer, it says so. And it can transfer the caller to a human who can pick up where the AI agent left off.

‍

An AI agent in the contact center that can successfully handle and resolve a wide range of Tier 1 conversations and issues on its own offers significant value. We’re still in the early days of these AI agents, but they can already automate complex interactions, from fluid conversations, through flexible problem-solving, to resolutions that satisfy customers. They won’t make the types of mistakes we saw in the examples above. And they’ll only get better from here. 

So, what’s the catch? It can be difficult to differentiate between these categories of solutions to identify which ones live up to the name AI agent. Here’s one clue to look for – at each turn in the conversation, an AI agent worthy of the name can be a little slower to respond than the other types of solutions. It’s taking the time to ensure safety and accuracy. So, it’s a good idea to maintain some healthy skepticism when you encounter an especially cool conversational demo. You’ll want to push the solution to see whether it makes things up or has sufficient safety mechanisms to give reliable, grounded responses.

The solutions that combine natural conversation and the ability to action on the customer’s behalf with robust safety mechanisms are the future of the contact center. They deliver fluid experiences with the flexibility to adapt in the moment, while maintaining safety and accuracy. And as fast as AI solutions are improving, the response speed will come, probably sooner than we expect.

Get past the vague language and ask the right questions when choosing an AI agent solution

Learn why these key questions are critical