Artificial intelligence (AI) is now firmly established in the workplace and in many other areas of daily life. While many people use it routinely, AI continues to evolve. While generative AI represents the most common form of use, agentic AI is now increasingly gaining ground.
This article compares both concepts, highlights their differences, and explains how the two forms of AI can be used intelligently or combined with one another.
Agentic and generative AI compared
If the AI world were an orchestra, agentic AI would make it to the conductor’s podium, while generative AI could technically play an instrument flawlessly when instructed. Agentic AI can make decisions, whereas generative AI depends on human input (prompts).
What is generative AI?
Generative AI refers to artificial intelligence that creates something in response to human input, such as texts, images, audio files, or code. It learns from large volumes of data (big data) and uses natural language models (large language models) to generate natural and human-like results.
Even though generative AI is highly adaptable and can produce different styles, it lacks the ability to act autonomously and independently execute multiple steps. Key use cases include support content such as knowledge bases, conversations, the creation of marketing materials, or product documentation.
What is agentic AI?
Agentic AI has the ability to make decisions and act autonomously and proactively to achieve specific goals. It plans, thinks in multiple steps and with contextual awareness, and independently executes sequences of actions. This is enabled by natural language processing, machine learning, and knowledge representation technologies.
Agentic AI does not depend on human input but independently takes over task frequencies and the next logical steps. For example, as a virtual agent it already comes very close to human capabilities, but it is still heavily dependent on predefined structures and data, which means it too reaches its limits when complexity is high.
Examples of agentic AI applications include autonomous vehicles, virtual agents, and task-specific copilots. Agentic AI is increasingly being used in customer service, ITSM, and cybersecurity.
The differences between the two concepts
Agentic and generative AI build on each other and work sequentially together for certain tasks. They are therefore neither opposites nor different evolutionary stages of artificial intelligence, but rather different forms that each serve specific purposes.
Autonomy is the key difference between agentic and generative AI: agentic AI applications execute actions in a goal-oriented manner, whereas generative AI specializes in creating outputs (results) based on inputs (prompts).
The following table shows the differences between the two.
From a technological perspective, agentic and generative AI share many similarities, such as LLMs, NLP, machine learning, and statistical pattern recognition. Unlike agentic AI, generative AI includes models for content generation. Agentic AI, however, surpasses generative AI with planning algorithms, memory modules, API integrations, reinforcement learning, and goal-oriented autonomous systems.
From a technical standpoint, agentic AI is therefore actually better equipped than generative AI. For users, the challenge lies in leveraging these strengths in a targeted way.
To get the most out of AI capabilities, a hybrid approach is recommended. For example, agentic AI can make a decision that generative AI then immediately implements. Following this principle, many workflows and processes can be automated.
Agentic and generative AI in ITSM
Both forms of artificial intelligence are ideally suited for ITSM, where they build on each other and complement one another. While generative AI has already established itself as a staple and productivity booster in ITSM, agentic AI is gradually making its way as a new technology, with its most exciting alternative being its role as a comprehensive AI agent in ITSM.
Generative AI in ITSM
In ITSM, generative AI can be used in the following ways, among others:
- Summarization of content such as tickets: Some ticket content, documentation, etc., is extremely long. A quick overview saves time and enables timely responses and solution approaches. Concise AI-based summaries quickly lead to the essentials.
- Response generation: AI-generated responses are sometimes better and sometimes worse. However, if they have a solid foundation and are briefly reviewed by a human, they enable fast reactions that add momentum, direction, and important solution approaches to the overall process.
- Classification and prioritization: Here, generative AI analyzes tickets in seconds, assigns them to the correct category, and gives them a priority.
- Real-time translations: Being able to translate messages instantly prevents language barriers and enables direct communication in the preferred language, paving the way for faster resolution.
- Self-service and knowledge bases: This area focuses on both well-known AI chatbots and knowledge base content that generative AI can quickly create, summarize, and update.
- Sentiment analysis: This is an exciting area of generative AI that allows conclusions to be drawn from moods and emotional undertones in messages. Based on such initial assessments, people can approach certain tickets in a prioritized and adapted manner.
Agentic AI in ITSM
Agentic AI enables more efficient, stable, and user-centric ITSM with a higher degree of automation and better service quality. This allows ITSM and enterprise service management (ESM) to be operated autonomously, proactively, and at scale.
The key differences: agentic AI independently takes over operational tasks, detects problems early, and gives IT teams more room to focus.
The most important areas of application are listed below:
- Agentic AI autonomously handles incidents and requests, including solution suggestions.
- It proactively detects problems and prevents disruptions.
- Agentic AI monitors security and responds quickly to potential threats.
- AI copilots support IT teams by automating specific processes and workflows and ensuring quality.
- Through intelligent self-service and intelligent knowledge management, solutions can be developed quickly and independently.
Generative vs. agentic AI in ITSM
Generative AI already has impressive capabilities in ITSM for creating, editing, summarizing, or translating content, but it is heavily dependent on human input.
Agentic AI goes a step further, acting autonomously and proactively in defined areas and paving the way for intelligent automation.
Both agentic and generative AI can be extremely beneficial for ITSM, although competent employees are still required and, especially in the case of agentic AI, a higher level of maturity is often necessary, as it is still at a relatively early stage.
Best practices for usage
When used correctly, agentic AI acts as a problem solver that operates autonomously and understands contexts. Generative AI does not have this level of autonomy, but due to its creative power it proves extremely helpful in many tasks.
The following practices may prove useful in benefiting as comprehensively as possible from both types of AI or their combined effect.
#1: Make a conscious distinction between agentic and generative AI
Many users make no distinction at all when it comes to artificial intelligence. But as AI is now used in many different forms, we must make distinctions in order to truly unlock its potential.
Agentic AI excels in decision-making and task sequences, making it valuable as an AI assistant, for example. Generative AI, however, also has a strong supportive effect through the creation of content and information, although it is not process-oriented and depends on inputs.
Agentic and generative AI can also be part of the same workflow and produce results that neither would have achieved on its own.
#2: Deploy technology with a strong focus on use cases
A good team is characterized by all members contributing their individual strengths while enjoying a high degree of creative freedom, without sacrificing strong, guiding leadership.
AI can be classified in this model: generative AI handles predefined tasks and creates content, agentic AI takes over workflows and project management, and humans define goals, strategies, and directions as the leadership instance.
According to this principle, teams should briefly assess which role technology can play in a given task—and how this can be achieved in a strongly use-oriented way. Various AI applications have great potential, but they require the right direction from humans to unlock it.
#3: Rely on flexible models
AI has moved beyond the hype and trend stage, creating a certain pressure to act for many organizations. Adapting to the new status quo is certainly a recommended approach, but factors such as maturity (for example, the individual ITSM maturity level) must be taken into account.
In practical terms, this means implementing AI on a broad scale only once the prerequisites have been created and processes are well established. Flexible models are suitable for adapting to AI, allowing different AI applications—whether generative or agentic—to be tested, evaluated, and implemented iteratively.
#4: It doesn’t always have to be AI
At first glance, this may seem counterintuitive when discussing best practices for generative and agentic AI. In fact, however, we should differentiate not only between these two technologies, but also between them and the human component. There are simply many tasks that are not suited to AI, which highlights typical human strengths all the more.
Examples include complex service cases that require tact and empathy, the design of nuanced strategies, or the development of unique solutions in a specific field.
#5: Review and evaluate AI results
Even though agentic AI achieves impressive autonomy, a thorough review of AI results is still recommended in many cases, especially for critical, sensitive, and emotionally charged topics.
Conclusion: Using potential appropriately
Generative AI and agentic AI are not competing models. Rather, a productive symbiosis is required so that the combined capabilities of both can deliver unprecedented added value in comprehensive AI-supported IT solutions. This may sound somewhat visionary, which is justified, as agentic AI is still in its early stages.
Even though agentic AI is technologically more comprehensive and complex than generative AI, they are not different evolutionary stages but similar technologies that serve somewhat different purposes: generative AI unfolds its strengths when organizations want to make input-based processes more productive, while agentic AI delivers autonomous processes that operate entirely without human intervention.
AI applications are now associated with a certain pressure to act, as they are spreading rapidly and organizations must keep up technologically. The approach “haste makes waste” is advisable: it is positive when implementations happen quickly, but they should also be accompanied by appropriate maturity and goal orientation.
Both generative AI and agentic AI show great potential, especially when working together. Organizations are now called upon to create the right conditions and unleash this potential.
