Artificial intelligence (AI) is developing rapidly, influencing numerous areas of life and fundamentally changing work processes. AI agents are among the most promising innovations – autonomous software programs that independently process data, make decisions and perform actions by interacting with their environment. They take on repetitive tasks and ensure structured processing of information – without any human intervention.
Basics of AI agents
AI agents typically consist of several core components:
- Perception: the agent takes in input data such as text, images or sensor data.
- Processing and decisions: It analyzes the collected data, draws logical conclusions and develops solutions. Modern AI agents often use large language models (LLMs) that understand complex relationships.
- Action: Based on the analysis, the agent carries out appropriate actions to achieve the defined goals.
The core task: structuring unstructured data
A central problem in many companies and authorities is the handling of unstructured data. Emails, PDFs and other documents contain valuable information, but without a clear structure they have to be entered and processed manually. AI agents solve precisely this problem: they extract content, convert it into a standardized format and integrate it seamlessly into existing systems. This creates usable information from confusing data – automatically, efficiently and error-free.
- One example from public administration: the AI-supported Foerderfinder (funding finder) developed on the basis of the mgm A12 framework. This tool analyzes funding programs from PDFs and extracts relevant content in order to transfer it directly into a structured database. Manual entry is no longer necessary – the entire process is efficient and automated.
Tax administrations are currently discussing a ban on emails in official processes, as they are not secure and cannot be authenticated. Against this backdrop, AI agents offer an interesting solution: users upload their documents to a secure portal and the system automatically structures the relevant data.
- Another example from the insurance industry is the mgm AI Assistant, which is based on the same principle as the Förderfinder. Brokers often receive hundreds of emails with requests for quotations. These have to be transferred manually to various portals. AI agents take over this process, analyze incoming messages, recognize relevant information, structure it and automatically transfer it to the right system.
However, these two use cases are only part of the overall picture. AI agents can optimize processes in many other areas where large volumes of documents need to be processed.
Progress through foundation models
In the past, the development of such systems required extensive training of individual machine learning models. However, with the introduction of foundation models such as GPT, the process has changed. Instead of spending months training individual models, it is now often sufficient to control existing models in a targeted manner using prompting.
An impressive example: an earlier mgm project for funding analysis took six months to develop and did not produce satisfactory results. With modern foundation models, comparable solutions can be implemented within a few weeks – with significantly higher quality.
Future prospects: AI agents as drivers of digital transformation
AI agents are only at the beginning of their development. Companies and public institutions that adopt this technology early on will secure massive efficiency gains. The combination of AI and low-code approaches such as A12 makes it possible to implement customized solutions faster than ever before.
The next step? Away from isolated individual solutions and towards a comprehensive understanding of AI agents as universal tools for data structuring. Whether insurance, administration or other data-intensive industries – wherever manual processes dominate, AI agents ensure automation and efficiency.
Conclusion: AI agents are far more than simple automation tools. They enable intelligent processing and structuring of data – quickly, reliably and with enormous potential for the future.
Would you like to find out more about specific use cases or do you have questions about integration in your company?
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