Agentic AI in Business: Rethinking Productivity

Artificial intelligence has already made significant advances. It really hasn’t been long since we discovered the immense benefits of generative AI with the launch of ChatGPT. But with agentic artificial intelligence, a new chapter is beginning. Systems that can operate on their own open new opportunities for efficiency, automation, and innovation.

For businesses, this raises some important questions. How can such intelligent agents be used effectively? And could humans retain control in this case?

The essence of agentic AI

The term agentic AI describes systems that can pursue goals, make decisions, and adapt their behavior to changing contexts. In fact, they act much like human experts.

While conventional AI typically relies on fixed algorithms, linear decision trees, or simple rules, this new type combines multiple elements: powerful large language models, context-sensitive decision-making, connections to real-time data, and often user or system interaction.

There are AI agents at its core. Such software entities act autonomously, analyze information, take relevant action, and monitor all significant processes.

In contrast to conventional automation solutions that rely on precise inputs, AI agents can work with incomplete data, interpret objectives flexibly, and address deviations promptly.

So, the main characteristics to keep in mind are:

• Autonomy: It’s objectives that matter when deciding on certain actions, not rigid rules;
• Proactivity: Agents don’t wait for commands; they simply perform the necessary operation if they recognize a need;
• Context sensitivity: Actions are guided by a specific situation, not by pre-defined sequences;
• Learning ability: AI agents continuously adjust their behavior based on circumstances and requirements.

The evolution of agentic AI

This evolution can be traced back to earlier advances in automation and language technology. Its roots lie in the first chatbots, which used simple rules and decision trees to mimic simple talks. Still, those were highly constrained and relied on pre-written responses and fixed patterns. They already demonstrated the promise of machine–user interaction but lacked profound understanding and context awareness.

Over time, advances in artificial intelligence have made significant progress possible. The rise of LLMs such as GPT-3 and GPT-4 has brought a substantial transition from rules to reasoning. Trained on massive datasets, these models have introduced an ability to comprehend, generate, and adapt language in ways that feel more human. This breakthrough has made the emergence of new technology possible.

Parallel achievements in machine learning have strengthened this evolution. Techniques like reinforcement learning now allow smart systems to learn by trial and error, refine their strategies, and optimize their performance. Meanwhile, growing computational power and sophisticated data processing has made it feasible for AI to operate at enterprise scale.

Agentic AI vs. generative AI

Generative AI has shown how powerful smart technology is in handling language, images, and data. LLMs can create breathtaking content or summaries, they can also analyze texts based on inputs and predefined workflows.

Agentic AI goes even further. Instead of merely responding or producing content, these systems act on their own and have a clear purpose. An AI agent analyzes a specific situation, makes decisions, uses tools, and independently carries out multistep processes—much like a typical person would when problems arise.

Thus, the main difference lies in autonomy. Generative AI always depends on human instruction and responds accordingly. Agentic AI, in turn, can initiate, coordinate, and carry out numerous tasks absolutely on its own. It won’t ask you if you need assistance, it can perfectly decide when help is needed and what actions to take.

It is based not only on data processing but on interaction, adaptability, and goal pursuit. It links analysis, planning, and action.

Why is agentic AI so valuable for businesses?

Using such a remarkable innovation is like hiring a powerful team to strengthen your current personnel. Once you decide to integrate it into your organization, you’ll gain immense support. It’s almost as if you had new employees who never get tired.

And it’s truly important to have such assistance nowadays. Many companies still face significant friction caused by manual processes, isolated tools, or rigid automations. Agentic AI connects data sources, understands business workflows, and takes action independently. The best part is that human intervention is minimal.

Some key advantages include:

1. Smart automation and optimization

AI agents are especially useful when performing such tedious tasks as categorizing emails, scheduling appointments, or preparing materials. They understand content, recognize patterns, and select the most sensible next step.

2. Quicker, well-grounded decisions

Large volumes of data are analyzed instantaneously, suggestions are provided, or operational decisions are made autonomously, for instance, in market monitoring.

3. Relieving specialists and teams

Intelligent automation eliminates time-consuming routines. Specialists can shift their attention to value-adding tasks while AI agents run in the background—for enhanced control and optimization.

4. Scalability and adaptability

Agentic systems can flexibly adjust to new tasks and circumstances. You can use a well-configured AI agent in marketing as well as IT. Each has a different focus but uses a similar methodology.

5. Interaction instead of isolation

It also enables efficient interaction with users or other software, creating coordinated, dynamic workflows with high efficiency.

How agentic AI systems work in practice

Such AI systems combine several proven technologies to not only automate and handle complex tasks but also manage them autonomously. Their special feature is the ability to continuously learn from context and feedback and respond flexibly.

The technical foundation includes, among other things:

• LLMs like GPT or Claude that act as the “brain”;
• APIs and data sources from CRM, ERP, or BI systems that provide context-relevant information;
• Interaction modules that enable communication with users, machines, or other agents;
• Rules, goals, and policies that define the framework for action and structure goal pursuit.

A typical agentic AI system consists of several specialized agents. Some collect and process data, others control operations, coordinate resources, or optimize workflows in real time. They can be scaled or adapted whenever needed.

Agentic automation: the next evolution in process optimization

This is one more important point worth mentioning in this context. Unlike conventional automation, which is perfectly suited for structured and repetitive tasks, agentic automation significantly improves the management of complex, unstructured processes, including workflows across systems like CRM and ERP.

At its core, it is a collaborative ecosystem. RPA bots handle the execution of predefined tasks such as gathering data, logging into systems, or processing transactions. In turn, AI agents use this data to analyze situations and make autonomous decisions. But there is one more critical element—human experts. They are still necessary to set objectives, ensure compliance, and intervene when nuanced judgment is required.

So, if you want to achieve even greater efficiency in your operations, you need to orchestrate seamless cooperation between people, robots, and AI agents.

Some application areas

Agentic AI use cases are various, and the application across industries keeps on growing. Here are just a few examples where it is already delivering impressive results:

Sales automation

An autonomously acting agent monitors sales data, interprets market trends, and proactively suggests actions for lead generation. At the same time, it creates personalized follow-ups and coordinates with the CRM. Interaction happens in natural language or via dashboards.

Digital healthcare

Agentic AI can relieve medical professionals by analyzing large amounts of data from patient records, studies, and diagnoses. This way, it recognizes connections and provides well-founded recommendations for diagnosis and therapy.

Pharmaceutical research

With the help of agentic AI, the development of new drugs can be significantly accelerated. It analyzes extensive biological and chemical data sets, identifies potential targets in the body, and delivers predictions about the potential effectiveness of substances.

4.0 Manufacturing and Industry 4.0

In production, such systems manage machine fleets, monitor production parameters, and adjust processes in real time. In case of material shortages or quality deviations, they intervene proactively, order spare parts, or initiate maintenance.

Autonomous driving

Thanks to this innovation, vehicles can continuously capture their surroundings, perform driving maneuvers independently, and gain new insights from every kilometer traveled.

Logistics and supply chain management

Supply chains are dynamically optimized: traffic data, weather conditions, and inventory levels are incorporated into analysis. This enables flexible adjustments of routes or transport modes. If delays threaten, customers are automatically informed. Everything runs efficiently, strategically, and in close coordination with ERP and BI platforms.

Recruiting and HR management

Conventional tools merely filter candidates. Here, truly independent evaluations take place: applications are analyzed considering team composition, development potential, and company culture. Suitable candidates are suggested, and interview appointments are coordinated automatically.

IT operations and system monitoring

Multiple agents work together to analyze server logs, detect anomalies, and intervene if needed. For instance, one of them opens a ticket, another one prioritizes it, and a third one communicates with the team or external service providers—quickly and around the clock.

Customer service

Another example is automated analysis of inquiries and generation of appropriate responses. Especially in omnichannel support, this approach increases response speed and significantly lowers operational expenses.

Process automation in controlling or procurement

Contract data, payment flows, and supply chain information are continuously analyzed. In case of irregularities, agents intervene automatically, offer alternatives, or initiate new processes.

What these scenarios have in common: such systems don’t act according to fixed rules but respond flexibly to situations, set priorities, and continuously adapt. As a result, businesses obtain solutions that truly think along and make processes noticeably more efficient.

Challenges in implementation

Although agentic AI offers enormous potential, its implementation still involves various challenges. Companies face strategic, technical, and cultural questions:

• Technology selection: Which platform fits the existing infrastructure?
• Data foundation: Is the available data sufficiently structured and up to date?
• Governance: Who controls the decisions made by agentic AI? How is quality assurance ensured?
• Responsibility: Who is liable if autonomous decisions cause misconduct, damage, or ethical conflicts?
• Acceptance: How can AI agents be integrated so that employees see them as support, not as a threat?

Therefore, companies must define new responsibilities and actively manage this transformation. This is a prerequisite for sustainable success.

Even though autonomous agents make decisions, the final authority always remains with humans—whether through targeted control mechanisms, clearly defined system architecture, or transparent decision logs. Only in this way can trust, security, and legal clarity be ensured.

Agentic AI is therefore not a “plug & play” product. That’s why you should consider professional AI consulting, effective integration, and in many cases, custom development. For that, you should cooperate with an agentic AI company developing feature-rich tailored solutions.

How Andersen supports agentic AI development

Andersen guides companies in developing, implementing, and scaling innovative AI solutions. We continuously expand our knowledge and expertise. Since the emergence of agentic AI, we have been specifically enhancing our know-how in this field as well.

Here’s how we can support you in deploying this technology:

Professional consulting and feasibility analysis

Our team works with clients to analyze which business areas can benefit meaningfully from agentic AI.

Prototyping and piloting

In short sprints, we create adaptable pilot solutions. These can be simple agentic workflows or complex, multi-layered AI agent networks. We give special attention to user-friendliness and data integration.

Scaling and long-term support

After a successful pilot phase, systematic company-wide implementation follows. We complement this with continuous analysis, optimization, and product development.

Development of customized solutions

Not all requirements can be met with standard tools. Therefore, Andersen develops agent-based applications with individually tailored LLMs, APIs, and user interfaces as needed. This tailored approach offers clear advantages, especially in areas like financial analysis, production planning, or risk assessment.

Our goal is to create efficient, secure, and sustainable products tailored to the exact needs of our partners.

Conclusion

Companies that embrace agentic AI early can achieve remarkable efficiency in their processes. What’s more, they will definitely see genuine leaps in innovation.

Implementing such systems requires technical expertise and a deep understanding of processes, technologies, and the human factor. So, collaboration with experienced IT partners is crucial. They bring not only technological excellence but also the necessary strategic consulting.

Last but not least: agentic AI does not replace human professionals—it complements them. As an intelligent partner in the digital enterprise, it strengthens teams sustainably.

FAQ

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How does agentic AI differ from traditional AI?

Conventional artificial intelligence generally operates in a reactive mode. It analyzes input and provides a result when prompted. Agentic AI is much more powerful and independent: it assesses situations on its own, makes the necessary decisions, and then carries out multistep tasks. Thus, it acts more like an autonomous collaborator than a passive tool.

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Which industries benefit the most from agentic AI?

Agentic AI can add significant value across many sectors, especially those dealing with complex, data-rich environments. Industries like manufacturing, pharmaceuticals, logistics, finance, and healthcare stand to profit the most, as well as any area relying on adaptive process optimization and intelligent automation.

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How does agentic AI work?

This technology functions as a network of specialized agents. Such entities analyze information, access relevant data sources, evaluate alternatives, and execute targeted actions—from initiating automated processes to providing strategic guidance—while continuously adjusting to evolving circumstances.

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In what ways does agentic AI safeguard data privacy and security?

Agentic AI frameworks incorporate strong governance measures, encryption techniques, and strict access management to secure confidential data. Careful human supervision, clear policy guidelines, and a secure system design are essential to ensure that data processing and automated actions stay compliant, transparent, and reliable.