Few technologies have generated as much excitement in customer service as artificial intelligence agents. According to Gartner, more than 90 percent of service and support leaders feel pressure from executives to implement AI, helping fuel the rapid rise of interest in agentic AI.
Despite all the hype, confusion over AI agents remains rampant. Today, many service and support leaders are being asked the same question: What is our strategy for agentic AI? Before they can answer that question, they need a clear understanding of what AI agents actually are, what they can realistically accomplish, and where they fit into modern customer service.
Defining AI Agents vs. Agentic AI
An AI agent is software that can evaluate relevant context, determine how to act, and take action to achieve a specified outcome. While traditional automation often relied on business rules and scripts, AI agents use reasoning large language models to determine the path forward. They might operate autonomously or semi-autonomously and can function either behind the scenes in enterprise systems or within customer-facing experiences.
Agentic AI, by contrast, is not a specific tool. It is an approach to designing AI-powered solutions that incorporates at least one AI agent somewhere in the workflow. In other words, an AI agent is the individual software component, while agentic AI describes the broader system or process in which that agent operates.
What AI Agents Can Actually Do
For customer service organizations, the appeal of AI agents is straightforward: They expand the range of work that can potentially be automated.
Traditional automation relies heavily on predefined rules. If an unexpected event occurs, the system typically cannot adapt. AI agents differ due to their use of reasoning models; if something unexpected occurs, they attempt to problem-solve until they reach their goal. AI agents are told which outcome they are supposed to accomplish and which tools they can access, but they make decisions about how to accomplish them.
Although AI agents can theoretically adapt to different scenarios, customer service leaders should avoid the misconception that AI agents automate entire jobs.
According to Gartner data, today's AI agents are best suited for tightly scoped tasks where objectives are clear, environments are constrained, and the required steps are limited. They are far less effective when work spans long time horizons, involves competing priorities, or requires consistent judgment across numerous exceptions.
As a result, most successful applications today involve AI agents working alongside humans, other AI agents, and traditional automation tools rather than independently managing complete business processes.
The Most Important Reality Check
Perhaps the most significant insight for service leaders is that AI agents are not intelligent employees in digital form. The industry often uses phrases such as "digital co-worker" or "AI employee," but these descriptions can create unrealistic expectations. AI agents are software . They lack the flexibility, judgment, decision range, or long-term planning abilities of human workers. Most remain semi-autonomous and still rely on human involvement at critical points within processes.
Likewise, AI agents do not learn from usage in the way many people assume. The underlying models do not improve automatically through daily interactions. Instead, organizations must continuously monitor, manage, and optimize these systems to maintain performance and effectiveness.
This reality has important implications for investment planning. Deploying agentic AI is not a set-it-and-forget-it exercise. Success requires ongoing governance, oversight, and operational management.
The Risks Leaders Cannot Ignore
The excitement surrounding agentic AI often focuses on efficiency and automation gains, but AI agents also introduce substantial costs and risks. Like other AI applications, AI agents are exposed to risks such as hallucinations, bias, cybersecurity vulnerabilities, data leakage, and escalating compute expenses. However, agentic AI introduces additional challenges because agents can take actions, not simply provide information.
The autonomy of AI agents can make problems harder to detect. Accountability becomes more complicated when multiple agents interact with one another across workflows. Poor governance can lead to agent sprawl, where large numbers of AI agents are created and deployed without sufficient oversight. And because agents may modify enterprise systems, mistakes can occur rapidly and at scale.
Perhaps most important, organizational readiness is the most common reason agentic AI initiatives fail. Fragmented systems, weak governance, inconsistent processes, unclear responsibilities, and poor knowledge management create greater barriers than the technology itself.
For customer service leaders, this means foundational investments remain critical. AI agents do not solve long-standing operational weaknesses; they often magnify them.
Where Customer Service Leaders Should Start
While many organizations are still experimenting with agentic AI, substantial potential exists for the technology to reshape customer service and support over time.
The most practical starting point might be back-office operations. Activities such as knowledge management, content generation, and identifying information gaps offer relatively low-risk opportunities to evaluate AI agents under controlled conditions.
Customer-facing applications are also promising, particularly in enabling more proactive self-service experiences. Rather than simply answering questions, AI agents might eventually identify customer needs, take appropriate actions, and initiate processes on behalf of customers.
However, leaders should approach these capabilities thoughtfully, recognizing that agentic AI is one service model among many rather than an automatic replacement for traditional automation approaches.
The future of customer service is unlikely to be defined by AI agents replacing human agents. Instead, it will be shaped by organizations that learn to combine human expertise, traditional automation, generative AI, and agentic AI in the right places. AI agents will not replace humans. More likely, humans who effectively use AI agents will replace those who do not.
Kim Hedlin is a research director in Gartner's Customer Service and Support Practice, and Daniel O'Sullivan is a director, analyst in Gartner's global Customer Service and Support Research Group.