What is an autonomous AI agent?

An autonomous AI agent plans, decides and executes tasks independently - without dedicated prompts. It uses tools, accesses data, remembers contexts and adapts dynamically. These capabilities make it possible to take on complex processes that go far beyond simple chatbots.

What can AI do in controlling?

AI in controlling recognizes patterns in large amounts of data, creates forecasts, automates reports and detects anomalies. In this way, it provides a sound basis for decision-making, saves time and increases accuracy - especially when it comes to forecasts, scenario analyses and budget deviations.

Are AI agents the future?

AI agents are seen as a central component of the next wave of digitalization. They combine generative AI with automation, tool use and the ability to learn - enabling productive, interactive systems that control processes independently and bring real efficiency gains.

A prompt is an explicit prompt that is used to control classic language models. Autonomous AI agents work differently: they plan their next steps themselves - without needing a prompt for each step.

Which tools do AI agents use?

Which tools AI agents use depends heavily on the use case. For example, AI agents can access internal systems, control APIs, interact with databases or use external services such as calendars, email clients or Jira. The important thing is that they can use these tools independently and depending on the context.

A humanoid robot works at a computer in a modern office, with blurred people in the background.

AI agents

Lean, smart and made for your company

AI agents from MaibornWolff - the perfect fit for your business

Would you like to automate your business processes and boost customer satisfaction? Then let’s work together to develop AI agents that are precisely tailored to your processes—without unnecessary technological overhead, but with maximum value for your business.

While artificial intelligence dominates the IT agenda, its impact is mixed—using AI without a fundamental strategy and clearly defined goals does not automatically solve problems, but rather amplifies existing inefficiencies and drastically increases the volume of digital waste. (MaibornWolff Study on Technology Efficiency, p. 21)

At the same time, the pressure is mounting: 61% of companies report that the use of AI tools has increased by 2025. (MaibornWolff Study on Technology Efficiency, p. 21)

MaibornWolff supports you with an interdisciplinary team of experts through every phase: from a clear strategy to technical development and seamless integration into your existing systems. Alwaystailored to your needs, efficient, and focused on your goals—in line with our guiding principle: Less Technology. Better Business.

Arrange a free consultation appointment

Customized AI agents - because standard is not always enough

Our AI specialists combine expertise from three fields: artificial intelligence, cloud technologies, and software development. This results in solutions that are not only technically impressive but also prove their worth in practice: robust, scalable, and future-proof.

The following applies: Those who view AI merely as a panacea will be disappointed—but those who see it as a specialized tool will reap significant benefits. (MaibornWolff Study on Technology Efficiency, p. 23)

This is because 19% of respondents currently do not see any tangible business value in AI. (MaibornWolff Study on Technology Efficiency, p. 23)

There is also room for improvement when it comes to KPIs: Only 48% of companies have defined KPIs to systematically measure the actual benefits of their software solutions. (MaibornWolff Study on Technology Efficiency, p. 13)

The good news is that those who approach it the right way reap the benefits: 42% of respondents already see increased efficiency in their processes as the primary benefit. (MaibornWolff Study on Technology Efficiency, p. 23)

Intelligence that fits

Our agents are tailored to your processes. They meet the highest standards of data protection, scalability and interoperability - and deliver reliable performance day after day, without breaks or after-hours.

Seamless integration

We integrate your AI agents directly into existing systems. Your workflows continue to run as usual, only faster, smarter and less stressful. And without having to rebuild familiar processes.

Measurable efficiency

We make success visible: KPIs show where you can save time, costs and nerves and still retain your customers for your brand. Our AI agents achieve different levels of autonomy - tailored to your needs.

Future-proof technology

We rely on scalable architectures and modern, interchangeable language models. This ensures that our AI agents remain fit even as requirements grow: today, tomorrow and the day after tomorrow. As reliable as on day one.

Have AI agents programmed: our range of services

We have been taking AI from buzzword to practice for over ten years. More than 70 successful projects later, we know: It's not just the technology itself that matters, but what your agents do in their day-to-day work and what benefits they bring to your business.

The focus is on agentic AI scenarios: In other words, AI agents that can interact independently, automate and make well-founded decisions.

After all, if AI is applied to inefficient processes, it will not result in greater efficiency, but merely in doing the wrong thing faster. (MaibornWolff Study on Technology Efficiency, p. 21)

Conversational & Autonomous Agents

We develop agents that communicate and act independently. Whether chatbot, voice assistant or complex system agent: our solutions think for themselves, interact in a context-sensitive manner and access external tools and data as required.

Agentic Automation

Decision support through AI agents

Well-founded decisions in real time: Our agents analyze current data, identify patterns and provide reliable forecasts, e.g. for maintenance, risk management or planning. Fully automated or as support for your team.
This is particularly true when it comes to modernization: By using AI agents, existing software landscapes and complex domains can be analyzed and modernized much more quickly. (MaibornWolff Study on Technology Efficiency, p. 23)

Technological foundations for our AI agents

We use a reference architecture we have developed for AI agents, which consists of three central components, among other things. Your customized AI agents are created on this basis.

Language model: serves as a decision center. Modern language models enable logical conclusions, planning of complex tasks and processing of multimodal input (text, images, etc.). We use appropriate language models for the task at hand.
Orchestration layer: coordinates processes, manages states and resources. This layer controls the behavior of the agents using a framework tailored to your use case.
Tools: enable access to databases, documents or other data sources so that the AI agents can go beyond their basic language model capabilities.

The Jevons Paradox strikes again: Since AI drastically reduces the cost and time required to create code and content, we are not producing less code in less time, but simply much more code overall—without proper governance, we risk an inflation of digital assets. (MaibornWolff Study on Technology Efficiency, p. 22)

59% of respondents fear that digital waste—specifically unused technical features, dead code, and redundant artifacts—will increase in the future as a result of AI. (MaibornWolff Study on Technology Efficiency, p. 22)

7 steps to your ready-to-use AI agent

From the first flash of inspiration to the ready-to-use AI solution: we accompany you in seven clear steps: transparently, at eye level and with tangible results after each phase. So you always know where you stand, and we know together how to proceed.

This requires the right foundation: 68% call for a thorough requirements analysis before the project begins. (MaibornWolff Study on Technology Efficiency, p. 19)
And: 66% agree that documentation must be an integral part of the Definition of Done. (MaibornWolff Study on Technology Efficiency, p. 19)

Successful AI projects from our practice

Every company has its own challenges - and this is precisely where our strength lies. We do not develop off-the-shelf solutions, but design each AI project according to the individual goals and framework conditions. Our AI projects show how ideas become concrete results.

To all references

STARTRAIFF: Business Intelligence for the sales force

We developed a web application for the sales force of an insurance company to improve their access to information. The application bundles and prepares data that is analyzed using AI.
To the STARTRAIFF reference

STARTRAIFF: Business Intelligence for the sales force

Dashboard

for more business intelligence

AI bundles

and analyzes customer data

Sales saves

60 minutes per customer visit

To the STARTRAIFF reference
inCTRL Solutions: Modernization of the IoT platform for water treatment plants

We are making the opsCTRL IoT platform highly resilient, maintainable, and capable of further development. New functions can be integrated into products more quickly.
To inCTRL reference

inCTRL Solutions: Modernization of the IoT platform for water treatment plants

Renovation

of the IoT platform

Maintenance & new features

go hand in hand

Change code quickly

thanks to CI/CD pipelines

To inCTRL reference
Health.exe: AI-supported platform creates training plans for patients

We developed a cloud-based platform that uses AI to automatically generate tailored treatment recommendations for patients based on a small number of medical parameters.
To the Health.exe reference

Health.exe: AI-supported platform creates training plans for patients

Project duration

5 months

AI in Healthcare

Services for orthopaedics

Simple integration

in practice systems

To the Health.exe reference
Research: AI-supported robotics for employees with physical limitations

With demographic change, employees with physical limitations need better support to remain productive. Assistance robots can help, but are not flexible enough. The KiRo4LeMi research project aims to use AI to dynamically adapt robots to individual changes in performance. Using digital models and "living personas", the AI optimizes robot operation in real time.
See robotics reference

Research: AI-supported robotics for employees with physical limitations

3 years

research project, funded by the Bavarian Ministry of Economic Affairs

Design of the robot

more individuality thanks to AI

Digital twin

helps to be able to react quickly during operation

See robotics reference
Siemens: AI demand prediction platform for industrial production planning

Siemens is looking to the future with the AI Demand Prediction Platform. Thanks to machine learning and AutoML, precise demand forecasts can be created for over 100 products and production can be better planned. Launched as a proof of concept, the platform quickly developed into a system that can be used productively. The self-service web application will soon be used in other plants.
See Siemens reference

Siemens: AI demand prediction platform for industrial production planning

Project duration

since February 2022

Proof of concept

in a few weeks

Time series prediction

for 100 different products

See Siemens reference
TÜV NORD GPT: Development of AI assistance

Find relevant information faster by chatting with documents? It's possible! The TÜV NORD GROUP uses GPT technology in the secure Microsoft Azure Cloud. With the aim of optimizing knowledge management and efficiency. The system enables new usage options within the testing group and is operated securely. Find out more about the innovative AI assistance system now.
See TÜV NORD reference

TÜV NORD GPT: Development of AI assistance

Project duration

since September 2023

33.000

GPT applications in the first month

ChatGPT Model 4

in the European Microsoft Azure Cloud

See TÜV NORD reference
digikoo GmbH: Apple Vision Pro for city planners

Planning cities smarter: Together with TUM, we developed the 3Digipad for Apple Vision Pro. It visualizes complex energy data in 3D and makes scenarios intuitively tangible. Dynamic building data and KPIs support urban planners in making sustainable decisions.
See digikoo reference

digikoo GmbH: Apple Vision Pro for city planners

Project duration

4 months

3D map with KPIs

Display building data dynamically

Energy data

Immersive visualization

See digikoo reference
ifm services: Remote maintenance of systems and machines

Maintenance, anywhere and at any time - together with ifm services, we developed a remote access solution for industrial plants. A small, agile team created a full-stack cloud application that combines intuitive operation and secure connectivity. The product celebrated its premiere at HMI 2024.
See ifm services reference

ifm services: Remote maintenance of systems and machines

Project duration

since March 2023

Remote Access

integrated in platform

Team

5 Developers

See ifm services reference
Bayernwerk: Knowledge management via teams

Experience must not be lost - Bayernwerk digitizes the knowledge of long-standing employees. MaibornWolff designed an intuitive MS Teams app with a clear UX/UI. Close collaboration, lived Scrum values and user-centered development make the app a success. Promoting exchange, optimizing processes - this is how knowledge transfer works today.
See Bayernwerk reference

Bayernwerk: Knowledge management via teams

Project duration

6 months

Target:

Identify implicit knowledge

Requirements:

a user-centered, intuitive and clear UX/UI design

See Bayernwerk reference
digikoo: A data platform for the Azure Cloud

Structured geodata, automated quality assurance, seamless provision - we developed a powerful Snowflake data platform on Azure for Digikoo. It makes analysis easier for data scientists and lays the foundation for precise forecasts and new use cases.
See digikoo reference

digikoo: A data platform for the Azure Cloud

Project duration

5 months

Climate change

Plan digitally and implement efficiently

Foundation data platform

Microsoft Azure Cloud

See digikoo reference

Typical challenges when introducing AI agents

AI agents have enormous potential - but in practice, similar obstacles keep cropping up. Companies are often faced with unclear objectives, have to deal with scattered data sources or must first build up the necessary understanding of AI internally. Regulatory requirements, non-scalable data architectures, unknown entry costs or the general question of how to get started also slow down many initiatives.

Our study paints a clear picture: German companies are drowning in complexity—investment in digitalization has been rising for years, yet operational productivity has stagnated in many areas. (MaibornWolff Study on Technology Efficiency, p. 2)

52% of respondents report that the use of inefficient software within their own companies has continued to increase over the past year. (MaibornWolff Study on Technology Efficiency, p. 5)

47% of IT managers and professionals feel overwhelmed by the sheer volume and frequency of new AI applications. (MaibornWolff Study on Technology Efficiency, p. 22)

This points to a classic SaaS sprawl scenario (unchecked proliferation of cloud applications), in which AI tools seep unchecked into business units as shadow IT without any central coordination. (MaibornWolff Study on Technology Efficiency, p. 22)

To ensure that innovative ideas do not fail in the face of reality, we support you wherever you need us: from the initial idea to the productive use of your agents.

How your company benefits from AI Agents

Whether you’re a small or medium-sized business or a large corporation, our AI agents help you streamline processes, reduce costs, and make informed decisions more quickly. Once the obstacles are out of the way, you can get started with a pragmatic approach that delivers measurable benefits.

In this sense, the key to new competitiveness lies not in adding more tools, but in the ability to eliminate the unnecessary. (MaibornWolff Study on Technology Efficiency, p. 6)

Those who succeed in freeing up budgets from legacy system management create the financial capacity for innovation and a faster time-to-market. (MaibornWolff Study on Technology Efficiency, p. 6)

And this is what it looks like:

Distribution

AI agents take over the pre-qualification of leads, automate quotation processes and prepare decisions based on data. This leaves your team more time for what no agent can replace: real customer relationships.

Customer support

Accounting & Administration

IT & Service Management

Technical AI agents automatically prioritize tickets, suggest appropriate solutions, and perform simple tasks on their own. This reduces response times and gives your team more time for their core business.
After all, 56% report a noticeable loss of time due to a lack of integration and the resulting system disruptions. (MaibornWolff Study on Technology Efficiency, p. 10)

The core problem here often lies in the lack of interoperability between systems—when software solutions do not communicate seamlessly, humans must act as an interface. (MaibornWolff Study on Technology Efficiency, p. 10)

A significant portion of IT resources is not directed toward innovation, but rather toward managing unnecessary complexity. (MaibornWolff Study on Technology Efficiency, p. 8)

Manufacturing & Maintenance

Purchasing & Supply Chain

Management & Strategy

Whether it’s forecasts, scenario analyses, or business cases, AI agents process decision-relevant information and support management with well-founded insights. This allows them to make more informed decisions, relying less on gut instinct and more on clarity.

Only 25% of experts believe that the benefits of their current IT projects significantly outweigh the costs involved. (MaibornWolff Study on Technology Efficiency, p. 10)

Nearly half of the respondents do not see any tangible added value for their company in the software solutions currently in use. (MaibornWolff Study on Technology Efficiency, p. 13)

Current guides on the topic of artificial intelligence

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Better estimate customer demand: definition, methods and added value of demand forecasting.
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FAQ: Frequently asked questions about AI agents

What is an autonomous AI agent?

An autonomous AI agent plans, decides and executes tasks independently - without dedicated prompts. It uses tools, accesses data, remembers contexts and adapts dynamically. These capabilities make it possible to take on complex processes that go far beyond simple chatbots.
What can AI do in controlling?

AI in controlling recognizes patterns in large amounts of data, creates forecasts, automates reports and detects anomalies. In this way, it provides a sound basis for decision-making, saves time and increases accuracy - especially when it comes to forecasts, scenario analyses and budget deviations.
Are AI agents the future?

AI agents are seen as a central component of the next wave of digitalization. They combine generative AI with automation, tool use and the ability to learn - enabling productive, interactive systems that control processes independently and bring real efficiency gains.
What is a prompt?

A prompt is an explicit prompt that is used to control classic language models. Autonomous AI agents work differently: they plan their next steps themselves - without needing a prompt for each step.
Which tools do AI agents use?

Which tools AI agents use depends heavily on the use case. For example, AI agents can access internal systems, control APIs, interact with databases or use external services such as calendars, email clients or Jira. The important thing is that they can use these tools independently and depending on the context.