A Brief Introduction to Chatbots and AI Agents
Chatbots are probably the best-known and certainly the most widely used form of artificial intelligence. They are based on generative AI, which is used to create content. They rely on large language models (LLMs), which use statistical patterns to generate natural language, and are remarkably adaptable thanks to machine learning (ML). In practice, they are characterized by the question-and-answer principle: Chatbots provide answers to questions asked by users.
Although many responses provide extremely useful approaches to solutions and move the conversation forward in a meaningful way, chatbots do not develop solutions themselves. Instead, they describe possible paths to a solution and generate useful content. The human user plays a key role too, since they must give the chatbot precise, purposeful instructions to obtain compelling results. The “science” behind this is called prompt engineering.
AI agents represent a clear step forward, even though simple versions can themselves function as chatbots. They can independently plan, reason, complete multi-step tasks, and pursue goals autonomously. Unlike generative chatbots, they are not dependent on human input and act proactively instead of merely responding according to the question-and-answer principle. They operate independently and adapt as needed. The differences can be striking: While a chatbot generates suggestions for solving a problem, an AI agent independently produces the solution itself.
AI Agents: What Makes Them Special
The groundbreaking aspect of AI agents is that they mark the transition from reactive AI to AI that truly takes action: They independently pursue goals, break tasks down into individual steps, make decisions, and determine for themselves exactly how a goal should be achieved. AI agents therefore do not merely provide answers; they develop solutions themselves.
The major advantage is that AI agents can take on complex and dynamic processes, such as those found in ITSM or customer service. In ITSM, for example, they can analyze, prioritize, route, and independently resolve tickets.
The most important capabilities of an AI agent can be summarized in three key points:
- Pursue goals without requiring every individual work step to be specified
- Independently select actions
- Evaluate progress and adjust their approach when necessary
Incidentally, an AI agent is not inherently the same as agentic AI. Agentic AI is the overarching concept, while an AI agent is a specific application of that concept. An AI agent can therefore be an (important) piece of the puzzle in an agentic AI system that pursues a goal by completing a sequence of tasks.
When Chatbots and AI Agents Make Sense
There are plenty of meaningful use cases for both AI agents and chatbots. Both perform important tasks, make everyday work life easier, and can sometimes deliver remarkably good results when humans and machines work together effectively.
Differentiation by Use Case
Exactly when a chatbot or an AI agent is preferable depends heavily on the specific use case. In general, the following distinction can be made:
Chatbots should primarily be the tool of choice for these tasks:
- Answering questions and providing information
- Offering advice and assistance
- Providing self-service and access to knowledge
- Collecting data and qualifying requests
AI agents, by contrast, prove useful for these tasks:
- Triggering standardized actions
- Performing multi-step tasks
- Handling tasks proactively
- Acting dynamically and making decisions
- Automating end-to-end processes
The Usefulness of Chatbots
Chatbots undoubtedly enjoy widespread use and recognition for all kinds of purposes. A distinction must be made depending on the use case: In ITSM and customer service, they prove particularly useful for knowledge management. Among other things, they enable easy access to a knowledge base, provide useful self-service content, and answer all kinds of user questions. Their usefulness always depends heavily on the underlying data. However, mature models in particular draw on extremely large volumes of data, enabling modern chatbots to provide reliable information.
The Usefulness of AI Agents
AI agents, by contrast, address a completely different use case. They are intended to simplify and accelerate processes, make them more consistent, prevent errors, and free up people’s time so that they have more freedom for value-creating activities. AI agents do not take over work in its entirety; instead, they focus primarily on workflows and repetitive, routine processes.. Human intervention is minimal, particularly once processes controlled by AI agents have become established.
Human activities do not become obsolete by any means. Instead, they shift toward creative, design-oriented, administrative, and innovation-driven areas. Employees design the processes that an AI agent will later take over, monitor them, review the results, and optimize the individual work steps. If the AI agent analyzes and resolves a ticket, it does so based on patterns it has learned from experienced employees. Over time, these patterns can be refined and optimized until AI-supported ticket handling runs almost perfectly.
How Best to Implement AI Agents
First, the good news: Although it is fascinating to explore the technical background and intricacies, users do not need in-depth knowledge here. However, they must understand what is and is not possible with AI agents and under which conditions.
For example, anyone expecting an AI agent to deliver rapid process improvements and immediate performance gains is misjudging the technology. Certain prerequisites are necessary to deploy AI agents effectively and strategically. These primarily include mature processes and a certain degree of internal AI maturity on which companies can build.
Principle: Automation, Not Improvement
Fundamentally, AI agents are not intended to improve processes, but to automate them: They can certainly reduce errors, save a tremendous amount of time, and produce an extremely high level of consistency. However, they must work with the input they are given. The principle is this: If a suboptimal process is automated, its errors multiply; positive effects do not simply appear out of thin air.
Your Own Prerequisites Are Decisive
Internal prerequisites are equally important: Creating business value requires strategy and governance, organized data management, employee AI expertise and buy-in, as well as effective integrations. Ultimately, companies should track the extent to which an AI agent delivers measurable value and establish clear monitoring for this purpose. On this basis, companies can pursue continuous improvement and, where appropriate, scale up.
The Importance of Choosing the Right Vendor
Finally, with innovative technologies such as agentic AI, it is particularly important to rely on established and trustworthy vendors. In the ITSM market, suitable options offer strong foundational technology and meet all basic ITSM requirements, including accessible and helpful service. Organizations should first meet the “must-have” requirements before working on the “nice-to-have” intricacies of an AI agent.
How Best to Use Chatbots
Chatbots are dialog-driven and, in areas such as ITSM and customer service, replace basic advice and assistance as well as significant parts of first-level support. They are ideally suited to self-service as an easy way to access a knowledge base containing instructions, documentation, and answers to frequently asked questions.
When it comes to complex issues involving experience, subjective perceptions, and rare incidents, however, competent human employees are essential. In such a scenario, a chatbot could still initiate contact and assign the case to the appropriate team or employee.
Many users are now highly familiar with chatbots and know how to use them effectively, although a few tips from the provider on how to achieve the best possible results can still be helpful.
AI Agents and Chatbots: No Decision Necessary
AI agents and chatbots are different applications of artificial intelligence, but they are not mutually exclusive alternatives. When comparing them, the purpose is not to weigh them against each other in order to choose one, but to use both as effectively as possible—according to their purposes, strengths, and value propositions.
In terms of pure business value, AI agents are certainly the more advanced technology: They perform multi-step tasks and independently make decisions based on learned patterns. By taking over entire workflows or independently resolving tickets in ITSM, they can generate enormous value for companies when used extensively.
At the same time, chatbots are enormously important in giving users fast and easy access to support. They significantly reduce the workload for agents, especially at lower support levels, and enable more to be accomplished in less time. In doing so, they have a direct impact on important ITSM metrics such as MTTR (mean time to resolution), CSAT (customer satisfaction), and ticket volume. In other words: With well-functioning chatbots, tickets are resolved faster, customers are more satisfied, and there are fewer tickets overall, reducing the workload.
So, when organizations ask whether they should use AI agents or chatbots, the answer is: both, if possible. However, if a firm choice between the two must be made, clear prioritization is required. AI agents promise the greatest value and are the more advanced technology, but a fairly high level of organizational maturity is needed to use them effectively.