Agentic AI: What is the next level of AI?
The world of artificial intelligence is undergoing profound change. While the focus in recent years has been on reactive and generative systems, a new class of AI systems is now coming to the fore: agentic AI .
These intelligent AI agents are characterized by the fact that they not only react to inputs or generate content, but also independently pursue goals, make decisions and carry out actions.
Key Takeaways:
-
Next evolutionary stage: Agentic AI goes beyond reactive and generative systems and enables autonomous action based on defined goals.
-
Combined AI capabilities: Perception, planning, decision making and action execution make Agentic AI a digital agent.
-
One clear difference: unlike traditional AI or RPA, Agentic AI is not just rule-based, but adaptive, adaptive and proactive.
-
Economic potential: Companies benefit from increased efficiency, automation of complex processes and new business models such as “Service-as-a-Software”.
-
Technologically ready: Advanced frameworks and multimodal capabilities are already enabling the practical implementation of agentic AI in companies.
What is Agentic AI? - Definition and differentiation
Agentic AI stands for a new generation of intelligent systems that not only process information, but also act independently – comparable to a digital employee who understands, prioritizes, makes decisions and actively carries out tasks.
These systems act purposefully, adaptively and with a high degree of autonomy – with minimal or no human intervention. At its core, it is about the ability of AI to not only “react” or “generate”, but to “act” in a self-determined way. This opens up new opportunities and strategies for companies.
Differentiation from other forms of artificial intelligence
To better classify agentic AI, it is worth comparing it with other well-known AI approaches. This makes it clear how fundamentally different agentic systems are in terms of their requirements and behavior:
Reactive AI:
- Works according to the principle “stimulus → reaction”.
- Reacts exclusively to current inputs, without remembering previous states.
- No opportunity to learn or plan.
- Example: A chess computer that calculates the best possible response to an opponent’s move, but does not develop a strategy over several moves.
Generative AI / GenAI:
- Produces new content such as text, images, code or music – based on learned patterns.
- Often works prompt-based and is strongly context-dependent, but not capable of acting in the narrower sense.
- Has no sense of purpose or planning behavior.
- Example: ChatGPT writes a product text on request, but does not know whether or how it will be used.
RPA (Robotic Process Automation):
- Automates simple, recurring processes on the basis of fixed rules.
- Cannot make decisions or react to unexpected situations.
- Does not react to changes in the process environment or new data.
- Example: An RPA bot transfers data from emails to an ERP system – as long as the format remains exactly the same.
AGI (Artificial General Intelligence):
- Vision of a human-like, universal intelligence with understanding, creativity and consciousness.
- Can think and act flexibly in any domain – comparable to human problem-solving ability.
- Still hypothetical today; often associated with ethical and philosophical questions.
Agentic AI:
- Combines perception, planning, decision and action in a closed cycle.
- Works purposefully, adaptively and independently within a specific area of application.
- Can act independently, react to new information and develop through learning.
- Example: An AI agent that analyzes customer data, creates a quote, synchronizes it with the CRM and answers queries automatically – all without human guidance.
The technological progress behind Agentic AI
The rapid advances in AI research and development form the basis for the emergence and spread of agentic AI. The focus is particularly on modern frameworks and architectures that make it possible to efficiently develop and orchestrate autonomous AI agents and integrate them into existing systems.
Important frameworks and platforms for Agentic AI:
- Microsoft AutoGen: Supports the orchestration of multi-agent systems and enables the division of labor among multiple agents across APIs and external tools.
- LangChain: Allows the linking of prompts, tools and memories to complex workflows based on LLMs. Modular, flexible and customizable.
- LangGraph: Uses graph structures to implement stateful processes – particularly useful in regulated industries such as healthcare or logistics.
- Microsoft Semantic Kernel: Focuses on semantic understanding and context-based reasoning. Particularly suitable for interactive applications with high contextual relevance.
This ecosystem is complemented by platforms such as Azure AI Agent Service, UiPath Agent Builder, Google’s Jules and open source initiatives such as Salesforce’s AgentLite.
This diversity shows: The technological basis for Agentic AI is maturing rapidly and is increasingly accessible – not only for tech giants, but also for start-ups and SMEs.
Innovation through multimodal capabilities:
Modern agents not only process text, but also images, audio and video. The integration of multimodal data sources enables a deeper understanding of complex environments – an important step towards true autonomy.
Build up AI expertise in your team!
Application in practice: industries and use cases
Agentic AI is no longer just theory. The first pilot projects or even productive applications are underway in numerous industries – with the aim of automating processes, improving customer experiences and reducing costs.
Industry examples:
- Customer service: Agents answer inquiries proactively, around the clock – empathetic, personalized and solution-oriented (e.g. Moveworks, Decagon).
- IT Operations: Automated problem solving, context-based IT support responses and adaptive system control.
- Healthcare: Support with diagnoses, patient monitoring and management of administrative tasks (e.g. Hippocratic AI, Tempus Labs).
- Finance: Autonomous risk analysis, fraud detection, investment decisions (e.g. Goldman Sachs).
- Logistics: route optimization, real-time data analysis and dynamic warehouse control.
- Education: AI tutors and personalized learning paths (e.g. Duolingo, Squirrel AI).
The new paradigm: "Service-as-a-Software"
One of the most exciting developments is the “Service-as-a-Software” concept.
These are no longer classic SaaS tools (“Software-as-a-Service”) operated by humans, but autonomous agents that provide complete services:
- An AI automatically handles the tax return.
- An insurance agent processes claims notifications completely independently.
- A sales agent dynamically adjusts prices and creates offers in real time.
This development is fundamentally changing the role of software: from a supporting tool to an autonomous service provider.
For companies, this means less manual work, shorter response times – and completely new business models.
Agentic AI in the company: The most important advantages at a glance
Agentic AI offers companies more than just efficiency gains. By using autonomous agents, processes can be designed more intelligently, decisions accelerated and customer loyalty improved.
- Automate processes – without losing flexibility:
Agents take on complex tasks independently – from data evaluation to decision-making. This reduces manual effort, speeds up processes and creates space for value-adding activities. - Make faster and more informed decisions:
Thanks to their ability to analyze large amounts of data and recognize patterns, agents provide decision-relevant insights in real time. This strengthens the responsiveness of teams and managers. - Personalize customer experiences at scale:
Agents understand user behaviour, adapt dynamically and interact in a context-sensitive manner – whether in customer service, sales or marketing. This creates true personalization on a large scale. - Enabling growth – with controlled costs
Digital agents can be replicated quickly, work around the clock and require no training or breaks. This allows services to be scaled without a linear increase in costs. - Increase quality and consistency:
Through rule-based decision logic and continuous learning, agents reliably deliver consistent results – regardless of the time of day, workload or employee changes.
Mastering the challenges of Agentic AI: What counts in deployment
The introduction of agentic AI is not plug-and-play. Companies need to keep an eye on technological, organizational and ethical issues in order to exploit the full potential safely and sustainably.
1. ensure transparency and control
Agents must not be black boxes. Their decisions must remain transparent – especially in regulated industries. Explainability, monitoring and clear responsibilities are crucial.
2. work with high-quality, fair data
Agents learn from data – and adopt its strengths and weaknesses. Distorted or incomplete data leads to incorrect decisions. Quality and bias checks are mandatory.
3. intelligently integrate existing systems
The benefits only unfold when agents are integrated into real workflows. Companies need scalable interfaces, clean data flows and a clear understanding of the roles of man and machine.
4. ensure security and data protection
The more autonomous a system, the more important it is to protect it against attacks, misconduct or data leaks. Agentic AI must be GDPR-compliant – and at the same time protected against manipulation.
5. taking the workforce with you
Technology is changing work – and often causes uncertainty. Companies should involve employees at an early stage, take fears seriously, build up AI skills through training and create trust through communication.
Do you need help with your AI strategy?
Ethical and legal implications of Agentic AI
As the autonomy of AI agents increases, so do the requirements for ethical responsibility, legal clarity and social acceptance.
Companies and developers face the challenge of designing Agentic AI not only efficiently, but also responsibly.
Responsibility and accountability:
Who is liable if an autonomous agent causes damage or makes a wrong decision? The legal allocation of responsibility is complex – especially in multi-agent environments or in the absence of human control.
Clear governance structures, chains of responsibility and guidelines for human oversight are needed.
Data protection and security:
Agentic AI often processes large volumes of sensitive data – whether in healthcare, finance or HR.
Compliance with data protection laws such as the GDPR is essential. At the same time, robust security mechanisms must prevent agents from being compromised or manipulated.
Algorithmic fairness:
Like any AI, Agentic AI is also susceptible to distortions in the training data. If these are not recognized, they can lead to discriminatory results. Transparent models, regular audits and the use of diverse, representative data sources are therefore essential.
Regulatory uncertainty:
While technologies are advancing rapidly, legislation often lags behind. The EU AI Act is a first step, but many aspects of Agentic AI – such as autonomous decision-making – are not yet clearly regulated by law. Companies must therefore prepare themselves for a dynamic regulatory landscape.
Conclusion: Agentic AI as a game changer
Agentic AI marks the next big evolutionary leap in AI development. It enables machines to act independently, make decisions and take responsibility – with far-reaching effects on efficiency, innovation and customer experience.
Companies that get to grips with this technology at an early stage can not only automate processes, but also rethink entire business models. However, this requires a considered approach to the associated risks – from data protection and transparency to social responsibility.
Agentic AI is not hype – it is the beginning of a new era of collaboration between man and machine.
You might also be interested in
