With the launch of AI Agents across many different customer service technology platforms, it’s important to understand how they work before you decide how you want to implement them for your CX team.

Some of the key questions CX professionals are asking with the rise of this new technology are:

• Why do I need more than one AI Agent?
  

• What are the benefits to having more AI Agents?
  

• Why do I need a team of AI Agents?
  
  

Each platform with AI Agents will operate in its own unique way as the platforms apply this technology differently. Here, we’ll dig into the framework of Kustomer’s AI Agents and the key reasons you may want to build a multi-Agent team.

How to think about AI Agents

First, it’s important to frame the setup of AI agents in your mind correctly to understand how the system works.

Many users might think that the quantity of AI Agents is related to processing time, and that having more AI agents would have the same experience as when a store opens a new cash register to process the line faster. However, that’s not quite right.

Instead, think of AI Agents like human members of a team that work together on a customer inquiry. But, with AI Agents, each represents a category of human support representatives that specialize in tackling specific issue types. The AI Agent is trained in specific operating procedures, it has the right terminology to use, it has access to just the relevant tools and knowledge, and it works together with other agents that are specialized in other specific parts of the resolution process.

Here’s a screenshot of how you would set up an AI Agent in Kustomer:

In Kustomer, you create AI Agent teams with a specific purpose. Here’s a visual of a simple team, with an AI Agent that communicates with the customer, one focused on returns, and one focused on refunds.

If multiple customers reach out about the same issue at the same time - for example, needing to return a product - the same AI Agent team will help all those customers at the same time. That means the AI Agent team scales to handle any number of incoming requests.

So, let’s say that a company only has one AI Agent team deployed. If a customer inquires about anything else outside of the scope of that AI Agent team, then that conversation can get handled differently depending on the AI Agent team deployment conditions the company has set up. The conversation could be resolved in the same way that it would as if the company had no AI Agents. The brand could still use other self-service options. If the customer still needs help from a human agent, then it would go into the queue and be routed accordingly.

More AI agents means more and more specialized team members, which means you can make more robust teams that handle specific customer service scenarios autonomously, and loop in a human when necessary.

Why you Should Have a Multi-Agent Team

Research from McKinsey calls out how AI Agents are the next wave of tech that will lead to not just more productivity, but more innovation. How? Multiple AI Agents will work in a multithreaded or parallel processing manner rather than a single-threaded manner.

They act like virtual coworkers that are completing whole sets of tasks, and they’ll advance to take on more complex work. They’ll go beyond being a chatbot that can address simple questions or automate intake forms to a valued contributor to your team.

This isn’t the
Kustomer system, but it’s a great visual of the concept:

Now that we have an understanding of the framework, let’s dig into the benefits of having more AI Agents on your team.

Specialization

When you have more team members, they can specialize rather than needing to be generalists. The same applies to Kustomer’s AI.

Each AI agent should be designed with a specific area of expertise. Here are just some examples of the specialties you can configure:

• Order tracking

• Returns

• Shipping queries

• Product recommendations

• Making, adjusting, and canceling reservations or appointments

• Billing

• Technical troubleshooting

• Warranty claims

• Account management
  

These can be customized to unique scenarios in your industry as well, pulling in information from your brand’s knowledge base. This structure of specialization results in more accurate resolutions because each agent is trained to be an expert in its domain.

Accuracy

In Kustomer, each AI Agent has a specific function and role that it plays on its team. You’ll need to provide specific instructions and access to specific data so it knows what to do and has the capability to execute the tasks.

In general, AI is good at doing one specific thing that it’s programmed to do. But when it tries to do two different things, it will try to fill in the gaps - but if it doesn’t have the proper knowledge, that’s when it can make things up, or “hallucinate.” When an AI has a more specific scope, there’s less of a chance that it can go wrong.

Breaking things down into subsets of instructions makes the AI more accountable, more specific, and more accurate when it gives responses. Thus, you’ll get more accuracy when you have more AI Agents that are all trained to help your customers with the very specific elements of getting a customer service issue resolved.

Adaptability

As customer needs evolve, it’s easier to tweak or update individual AI Agents rather than overhauling a single large AI system. If you need to update the instructions for existing agents or add new agents, you can do so without disrupting the existing structure. This modularity allows for more agile and targeted improvements.

Efficiency

One of the primary benefits is efficiency. With more AI Agents, you can automate a broader range of tasks across departments and functions. This means less reliance on human agents for repetitive inquiries. This enables companies to allocate human resources to high-value tasks while AI agents handle routine queries, driving down costs and boosting ROI on customer service operations​​.

With
Kustomer, the Enterprise and Ultimate AI tiers allow greater levels of automation, with each AI agent capable of being customized for distinct roles. These advanced workflows and enhanced automation capabilities allow for more complex support tasks, which might be required for larger companies or more diverse customer bases​​.

Scalability

Like all technological developments, as AI takes on more of your customer service inquiries, it frees up more time for your team to handle other higher-level initiatives that can allow your business to scale in other ways. In addition, with more AI Agents, the AI system can literally scale as well.

With multiple AI agents, you can add and train new agents to handle specific functions without requiring significant adjustments to your entire system. This means the infrastructure can grow with your needs as you launch new products and services.

A team of just 10 AI agents (as in the Professional AI package) provides limited task management capacity, suitable for basic
automations and straightforward workflows​​. You might put all of your AI Agents on teams to handle just one or two common scenarios.

In contrast, a team of a few dozen or few hundred AI agents can be beneficial for enterprises with a wide variety of product offerings, or multi-step processes to resolve nuanced customer service issues.

Conclusion

AI Agents are the next era of customer service tech, by taking time to understand how they really work, you can implement them more smoothly and efficiently. When you’re evaluating your options with AI Agent technology, consider how designing a system with additional AI Agents will allow superior scalability, specialization, and flexibility. It’s all powering your organization to deliver fast, proactive, and tailored support on a large scale.

If you’re ready to see how AI Agents can help your
CX team, schedule a demo now.