The future of AI agents is specialized

AI in customer experience is continually evolving—and how agents are built behind the scenes is changing, too. When service teams shifted from traditional automation to agentic service, they started by building one big bot that could answer any customer question. Now, that infrastructure is too generic for the needs of service teams, especially as AI agents take on more autonomous work. 

Like any high-functioning team, the autonomous workforce depends on specialized teammates: agents with deep industry expertise and context. These agents are powered by data and customized through each team’s connected systems. Four in 10 CX leaders say their agents can already orchestrate actions across multiple systems in a single flow. Even more (49%) want them to do so within the next 12-24 months.

However, many service leaders have structural barriers to overcome before then: 76% of CX leaders say siloed or hard-to-access data is their biggest barrier to scaling agents. Creating a connected knowledge foundation will not only enable more personalized support at scale—using customer history, intent, and context—but also deliver proactive experiences that anticipate needs before customers even have to ask for help.

To build specialized agents, organizations must:

Connect workflows across the business

The nature of work demands nuanced skills and complementary abilities. AI agents should reflect this. Specialized agents are a new kind of teammate, combining industry knowledge with the context and experience contained in an organization’s data.

But expertise alone isn’t enough. Agents also need access to the systems where work actually happens. Connecting workflows across CRM, billing, inventory, order management, knowledge, and other systems allows agents to move beyond simply answering questions to taking action on a customer’s behalf.

Design agents based on deep expertise

Just as organizations rely on people with different areas of expertise, they shouldn’t expect a single AI agent to excel at every type of work. Instead, agents should be designed around specific responsibilities, processes, and areas of knowledge.

Individual teams contribute the expertise that makes each agent useful. For example, your team might want an AI agent to handle subscription cancellations. In this scenario, billing can define cancellation and refund policies. Product can provide context on plan features and alternatives. Customer success can define which retention offers are appropriate and when. Support can own the customer conversation and determine when the interaction should be escalated to a human employee.

Create one connected customer experience

Customers don’t think in terms of channels, systems, or organizational silos. They simply want their problem solved. Specialized agents therefore need to work together as part of one connected service experience.

Connecting data, workflows, and channels gives agents a shared understanding of the customer and reduces the effort required to get help. A customer who starts in messaging and moves to voice, for example, shouldn’t have to repeat information they’ve already provided. Likewise, an agent handling a billing question should be able to draw on relevant order or support history when it affects the resolution.

The more context agents can carry across interactions, the more service can shift from reactive to proactive: recognizing intent, anticipating likely needs, and resolving issues before they become bigger problems.

The result is a more robust support organization—one where specialized AI agents and human employees operate as a coordinated team. By connecting systems and embedding expertise, service organizations can respond faster and operate at greater scale without sacrificing the quality of the customer experience.