What Is Agentic AI?
Figuratively speaking, agentic AI functions as the brain and hands of an AI application, interacting with each other to complete a sequence of tasks. It is based on a Large Language Model (LLM) and effectively takes the wheel to activate and synchronize various tools.
The following definition sums it up: An LLM agent executes tools in a sequence to achieve a specific goal.
The decisive factor is autonomous action, through which the AI uses context and goals to determine for itself which steps should be taken next. This eliminates dependence on rigid, predefined rules.
The Most Important Characteristics
Agentic AI has several defining characteristics that make clear its position as ‘agentic’ and its benefits.
First, it is strongly oriented toward predefined goals and autonomously aligns its actions with them. This works across multiple steps (breaking complex tasks down into subtasks) and requires only minimal human control. In doing so, the AI prioritizes and adjusts plans as needed.
It also independently uses tools such as databases, APIs, web search, and applications, while incorporating information from its environment and previous interactions. Agentic AI can take this information, along with intermediate results, into account across multiple work steps.
Here are some other important characteristics:
- Agentic AI does not merely select responses; it immediately plans the next actions and executes them.
- It checks results and corrects its approach or planning as needed.
- Agentic AI has multi-agent capabilities, allowing it to coordinate and delegate tasks with other AI agents.
- It requires guardrails such as permissions, human-in-the-loop concepts, monitoring, and termination mechanisms.
Areas of Application for Agentic AI
In general, agentic AI makes sense wherever use cases are more complex. When it comes to “simply” generating content, as is the case with many AI applications, generative AI is definitely the way to go. However, if the AI application is intended to independently pursue goals, execute actions, and make decisions, agentic AI takes center stage. This often also needs to happen across different steps and systems.
Agentic AI can typically be used effectively in the following areas:
- ITSM: Here, the focus is on analyzing, prioritizing, and escalating tickets, finding solutions, and implementing changes.
- Customer service: AI can understand customer concerns, gather information from different systems, and handle numerous cases independently or be used in a hybrid approach alongside employees.
- Cybersecurity: In this area, agentic AI can be extremely valuable because it can detect and investigate (potential) threats and, in some cases, independently initiate countermeasures.
- Supply chain and production: Here, agentic AI can optimize many things, from procurement and inventory to maintenance and production processes.
- Human resources: Agentic AI is already being used successfully for recruiting, onboarding, employee inquiries, and administrative processes.
In addition, there is the broader area of personal assistants. For example, agentic AI is suitable for researching topics, coordinating appointments, planning trips, or completing certain tasks across different tools.
Agentic AI vs. Generative AI
Generative AI primarily responds to requests, whereas agentic AI pursues a goal, independently plans and acts, reviews results, and autonomously adapts its approach. Where agentic AI executes actions, generative AI creates content. While agentic AI acts in a goal-oriented manner and solves problems, generative AI is pattern-based and generates data.
The two are neither opposites nor different evolutionary stages of artificial intelligence. In fact, close collaboration between the two is necessary for an AI agent, for example, to prove useful and increase efficiency. However, autonomy is the decisive difference, because agentic AI independently executes actions and may also use generative AI to do so, whereas generative AI only reacts to inputs.
Where generative AI provides answers, agentic AI independently completes tasks.
Benefits of Agentic AI
Using agentic AI not only relieves employees and enables (partial) cost savings, but also provides a genuine economic advantage: It enables more automation (up to hyper-automation), autonomous decisions, accelerated processes, and the parallel handling of many tasks and processes (good scalability). The decisive factor is that agentic AI independently works toward previously defined goals.
Here are some of the most important benefits of agentic AI:
- Those who use AI agents benefit from significantly less repetitive work.
- Companies can sometimes achieve substantial cost savings.
- Processes become more consistent and less prone to errors.
- Productivity increases by reducing opportunity costs.
- Employees experience relief from their workload.
- Customers benefit from faster, satisfactory responses.
- More tickets can be resolved per employee.
- New employees benefit from faster onboarding.
The Path to Profitable Use of Agentic AI
The decisive factor in adopting AI agents is not the size of the company, but rather an organization’s AI maturity. This can vary considerably from one organization to another. A key challenge and important differentiator therefore lies in improving this maturity – with measures such as these:
- create a clear AI roadmap
- optimize and standardize processes
- launch AI trials and learn from them
- identify and evaluate AI goals
The path toward agentic AI typically takes place step by step: Knowledge support typically marks the beginning of AI use (initially in generative forms), automation represents the next step, while the autonomous execution of task sequences is the culmination of this development.
This step-by-step logic is reinforced by the fact that companies that already automate processes with AI are almost twice as likely to use AI agents as companies that do not.
Today, the decisive factor is no longer simply using AI, but successfully transitioning toward AI that independently executes tasks.
Agentic AI in ITSM
Using agentic AI represents something like the second evolutionary leap in ITSM; the first was using AI at all and, for example, automating certain tasks with it. AI that does not merely respond to requests but actually resolves tickets represents a major step forward. At present, relatively few IT organizations use AI agents that independently execute task sequences and workflows. Those that have already reached this stage have a significant advantage.
In ITSM, AI agents can, for example, automatically classify and resolve tickets or identify and fix problems before they become incidents. IT employees therefore have more time for complex tasks that require a high degree of judgment, empathy, or strategic decision-making.
Due to its task structure, the maturity of the market, and its many rule-based processes, ITSM is ideally suited for implementing AI agents. They have significant potential that organizations should seek to leverage as fully as possible. Organizations must now create the necessary internal conditions to do so.
Outlook: Current Topics and a Forecast
Following the hype surrounding generative artificial intelligence, agentic AI currently occupies a dominant position among trend and future topics in the IT and technology sector. The term is used somewhat excessively in some quarters, but it justifiably holds a prominent position.
A Clear Understanding Is Essential
The challenge lies in clearly distinguishing agentic AI from other forms of AI, defining the scope of its benefits, and using it in a goal-oriented manner. There is currently a high level of enthusiasm, but without a fundamental understanding of agentic AI and consideration of topics such as IT security, governance, and reliable software engineering, that enthusiasm is not worth much.
To do more than merely participate in debates and instead gain tangible advantages, a clear understanding of agentic AI is essential. This does not necessarily require knowledge of technical details and intricacies, but it does require an understanding of how to use it as effectively as possible.
Collaboration Instead of Replacement
On the other hand, there is concern that AI agents could replace human workers on a large scale and make them redundant in many areas. Even though the capabilities of agentic AI are increasing and it already operates with a high degree of autonomy today, such a scenario remains far off. Humans are needed not only to control and monitor AI, but above all to perform tasks that are now shifting in nature. Specific expertise and skills such as empathy, creativity, and unconventional thinking are becoming increasingly important and can unlock significant performance potential when combined with AI.
For example, agentic AI in customer service can function as a language model that repeatedly calls tools, processes results, and works step by step toward a goal. It possesses autonomy, but not autonomous consciousness, which means that referring to it as a digital employee would be an overstatement. Instead, it largely relieves employees, who can then focus more on complex customer problems and concerns as well as value-adding and varied projects.
Future Forecast for Agentic AI
In the future, agentic AI will likely have an established position in the business world and offer expanded capabilities. Users may not even need to differentiate much between agentic and generative AI, but instead use both together as a matter of course: AI assistants will then become normal everyday companions and tools that employees use as naturally as they use a hammer or drive a car.
It is quite conceivable that many knowledge workers will have their own personal AI assistants at their side and thus receive support tailored precisely to their needs. In this sense, AI assistants could become less of a hype topic in the future and simply become part of everyday life, as they are already increasingly doing today.
Agentic AI has the potential to imitate human behavior to near perfection, allowing it to readily take on the role of an outstanding employee. This is not limited to rule-based and repetitive activities, as agentic AI makes decisions and acts with a focus on goals. When it comes to creative and innovation-driving processes, however, agentic AI will also reach its limits. This would require so-called strong AI, which remains purely hypothetical for now.
What Is Still Holding Companies Back From Agentic AI
In principle, there is nothing preventing many companies from broadly implementing agentic AI today. What is still holding many of them back can be summarized as problems with their own AI maturity. In this context, several practical problems are often particularly relevant for small and medium-sized companies, preventing them from adopting new technologies and innovative approaches.
This helps explain why, despite the now widespread presence of AI assistants, there is still considerable room for improvement in the implementation of agentic AI in many other areas. In this sense, agentic AI requires a certain amount of groundwork before it can be used effectively. This includes defining goals, providing context, establishing precise processes, and supplying sufficient training data. On this basis, a Large Language Model can ultimately be used effectively.
Conclusion: Agentic AI Independently Achieves Goals
Agentic AI is characterized by its ability to independently pursue goals defined by humans. Where generative AI provides answers, agentic AI completes tasks. Its multi-step or process-oriented nature is an important factor: “An LLM agent runs tools in a loop to achieve a goal,” is an apt definition by Simon Willison. Agentic AI can achieve a goal by independently determining which steps are necessary to do so.
Companies can benefit from agentic AI in many different areas. They must now create the necessary conditions for doing so. The following principle applies: Those who do not successfully use AI for basic automation will most likely also struggle to use it for task sequences that require agentic AI. In other words, sufficient internal AI maturity is an extremely important factor.
The effort is worthwhile, because the benefits of successfully using agentic AI can be enormous. It can also be assumed that this technology will become standard in the foreseeable future.
FAQ
Finally, here are some selected frequently asked questions about agentic AI.