AI agents are coming to your XM stack—what you need to know

 In just a few short years, integrating functional AI into business software has gone from science fiction to a common reality. Companies are now working on the next practical step in applied AI: agents. In experience management (XM), AI agents are beginning to shape how brands understand and improve customer and employee experiences. For XM professionals, that raises important questions: What exactly is an agent? How are they different from the tools you already have? And, most importantly, how do you separate the helpful ones from the ones that are mostly flash? 

What is an AI agent?

An AI agent is a system that uses AI models—often large language models (LLMs)—in combination with other resources to perform tasks with no or minimal guidance. It differs from traditional chatbots or automation tools in that it can reason through complex problems, take multi-step actions, and make decisions based on a goal. 

Unlike basic bots that respond to one instruction at a time, agents can work through objective-based sequences. They break tasks into smaller steps, gather the information they need, evaluate whether they’ve found enough to proceed, and carry out the next action—all without being told what to do at every turn.

What makes agents especially useful is their ability to use external tools and resources. While an LLM alone might be limited to generating text based on its training data, an AI agent can: 

• query a database (or databases) 
• run calculations
• perform statistical analysis

If instructions are vague or incomplete, a well-designed agent will ask follow-up questions to better understand the goal. And if it's uncertain about the next step, it may pause to confirm with the user before proceeding. This kind of interaction helps keep agents aligned with user intent and improves the reliability of their results.

In short, AI agents are built to do more than respond—they’re built to reason, adapt, and act in service of a clearly defined outcome.

How we got here: from bots to agents

AI agents are the next step in a long evolution from the rigid tools of early XM automation to more flexible, goal-driven systems. This history of development includes:

• Interactive Voice Response (“Press 1 for billing, 2 for support…”) systems helped route calls efficiently, but offered little in terms of user experience.
• Rule-based chatbots (Geico’s virtual assistant) followed scripts and handled basic tasks, but struggled if conversations veered from the script.
• Auto responders (“Thank you for contacting us—we’ll respond within 24 hours.”) acknowledged incoming messages but couldn’t actually solve problems.
• Virtual assistants like Siri and Alexa introduced natural language capabilities, but they were tied to narrow use cases and lacked deeper reasoning.

Each step laid the groundwork for today’s AI agents, which have evolved into tools that can understand goals, use external resources, and take action with minimal hand-holding.

How AI agents are changing experience management

AI agents are beginning to support nearly every part of the XM lifecycle. While adoption is still in its early stages, the potential is already showing up across a range of use cases from collecting feedback to resolving customer issues in real time. AI agents are helping teams understand customer and employee experience data faster—and take smarter actions in response.

Here’s where agents are already making an impact:

Collecting data

AI agents can provide guidance on survey design and strategy based on the data available to them, and as a result will uncover coverage gaps or patterns worth exploring. They can also collect feedback directly from customers or employees in a conversational interface, asking follow-up questions when necessary. Finally, real time AI analysis can also detect unusual trends in your data to flag and quarantine suspicious data from malicious sources or “data gaming” before it causes problems.

Why it matters: You get more relevant, complete, and reliable feedback data collected in a way that feels natural to the user.

Analysis

Modern XM programs are pulling in more data than ever before from an ever-expanding array of channels that include everything from open-text survey responses to social media comments and internal notes. AI agents can analyze all of it faster than humans, mapping trends, performance drivers, and friction points. It also becomes possible to compare data and find connections across functions between brand, customer, and employee experiences to see how they impact each other. Lastly, AI agents also maintain a more complex, contextual understanding of language across sources than what came before to better determine nuance.

Why it matters: Teams can spend less time reading and tagging and more time acting on what matters.

Tailored reporting

AI agents can make analysis more digestible by tailoring how insights are presented based on the audience. They can summarize results for executives, spotlight location-specific performance for managers, or pull out relevant themes for analysts—all without requiring manual rework.

Why it matters: This flexibility helps teams get the information they need, in the format they need, to act quickly.

Recommendations

As a follow-up to analyzing data, AI agents can suggest actions to improve performance based on patterns they’ve found and other examples they’ve been given for reference. However, the quality of these recommendations will depend heavily on the background knowledge available to the agent. Here, companies that can provide context and examples to their AI about what separates a good recommendation from a bad one will see more appropriate, higher-quality output.

Why it matters: Strong recommendations that help teams prioritize the actions that are most likely to move the needle are rooted in real expertise and high-quality data.

Notifications and syndication

XM programs often trigger alerts based on keywords or score thresholds. AI agents can expand this functionality by scanning more data, identifying new patterns, and routing insights to the right people based on relevance.

Why it matters: Stakeholders stay informed on emerging trends or need-to-respond-to problems without having to dig for them.

Case response

SMG’s extensive data set shows that customers who experience an issue that is satisfactorily resolved are 7 ppts more likely to return than customers who didn’t have an issue at all. AI agents can help manage responses—acknowledging issues, providing resolution options, and even initiating compensation depending on the context. These responses also can be integrated into the feedback stream to add new data into the system for future analysis.

Why it matters: Faster, more consistent follow-up improves customer satisfaction and builds a stronger feedback loop.

Quick note: Better input, better output
Like a new team member, an AI agent still needs onboarding, guidance, and the right tools to succeed. An AI agent’s quality depends on what it has to work with, both in data structure and background reference content. If your data is well-organized, standardized, and supported by rich examples, agents will be more accurate and more useful.

Limitations

For all their power, AI agents aren’t magic, nor are they plug-and-play. Like people, they rely on structure, clarity, and access to useful information to be effective. Organizations looking to deploy agents in their XM programs should understand what might limit their performance.

Bolt-on vs. AI-native design

Many AI agents are being layered onto existing platforms that weren’t originally designed for intelligent automation. In these cases, the agent can only work with the data and tools already in place which is often in incompatible or fragmented formats. This limits both the depth of the analysis and the usefulness of the agent’s actions.

AI-native design, in contrast, allows for greater functionality by having a data platform and systems that are designed to work across applications. This also allows the construction of AI programs that can be more effective, faster by not needing as much tailoring to individual databases or tasks.

Data platform construction

If the underlying data isn’t organized in a unified, consistent way, AI agents will struggle to do anything meaningful with it. Disconnected sources, inconsistent formats, and missing context all reduce the agent’s ability to analyze, interpret, and act. A strong foundation—one that’s built to support AI from the ground up—makes a major difference.

Informing and structuring for quality

With data alone, an AI agent will just identify any patterns and trends in the data it can find. Without guidance, the agent won’t be able to prioritize what’s most important to you. This is solved by providing statistical analysis tools and background expertise to an agent, allowing it to go from "What are customers saying?" to "What should I do?"

Making smarter decisions about AI in XM

AI agents are becoming a standard part of the toolkit for how experience management gets done. But as with any emerging technology, success will depend on how well agents are aligned to real XM goals, grounded in quality data, and built to actually support the people using them.

As vendors continue to roll out AI-powered features, the best XM leaders will ask:
Is this helping me collect better feedback? Understand it faster? Take action with more confidence?

The right solution is more than a smart-looking demo, it feels like a natural extension of your team, helping you get more value from the data you already have, and delivering insights you can trust.