Agentic AI as the basis for success for start-ups

853 of the 3568 newly created companies come from the software sector. However, they are not the only ones using artificial intelligence; in the survey, a third of all founders stated that they work with AI. Accordingly, the association's spokespeople also see this technology as the driving force behind the startup record.

If we look beyond the horizon, we can see that the startup scene in Europe as a whole is flourishing. The State of European Tech 2025 report, commissioned by Atomico and others, estimates that investments of 44 billion US dollars (equivalent to 38 billion euros) flowed into this sector last year. Funders now expect startups to work with AI and deep tech, according to the report. According to the Atomico report, 36 percent of European startup investments went into these two fields.

The start-up environment could hardly be better, but a record number of start-ups and rising investment sums do not necessarily mean that business models can be scaled up easily. Many start-ups fail to further develop their business after a successful first few years. In addition to a number of common challenges, the main ones are bureaucracy, data sovereignty issues and operating across national borders and economic zones. Startups must prove that their use of AI is based on solid, regulated data foundations and meets compliance requirements.

This is where Agentic AI comes into play. Embedding AI into the core of operations is the answer to many of these challenges and will be critical to growth in 2026.

Solid database before AI deployment

Start-ups that want to achieve this should focus on building their AI usage on a solid database based on a standardized data architecture. They find it much easier to make the necessary architectural decisions than established companies with corresponding legacy IT. This is where Agentic AI comes into play. From the outset, founders should think about how they can set up a strong data architecture, break down silos and embed AI as the core of their processes.

This foundation helps with the introduction of AI agents so that their outputs also meet expectations, i.e. to structure and optimize business processes more efficiently and accelerate decision-making. Start-ups that implement this will prevail over their competitors and ultimately be successful.

AI agents as innovation accelerators

By integrating AI agents as the core of their business processes right from the start, startups can scale faster than by using just one large language model (LLM). The reason for this lies in the standardization of the data required for AI agents. With this basis, the agents can make use of their unique, autonomous capabilities. They are trained using the company's own data. Potential can be quickly leveraged, especially in start-ups, if the agents are developed for specific tasks, they can also solve them, no matter how complex and specialist they may be. If the database is right, several agents can be linked together to solve even more complex challenges.

One example of this is the possible cooperation between a customer support agent and a forecasting agent. When a customer triggers a support case, the other agent can immediately calculate the costs, which can increase customer satisfaction through a faster response. Close cooperation between departments is important for scaling start-ups in order to further expand business relationships with satisfied customers. The use of AI agents can overcome limited resources with the human element and enable better service performance, which is essential for rapid growth.

However, it is not only customer contact that can be automated, but also a whole range of routine processes in the internal administration of the companies themselves. This enables not only management, but also investors, to gain a quick overview of liquidity, turnover, revenue and profit. The real-time insights lead to quick and well-founded decisions, which is worth its weight in gold for young companies and enables them to remain flexible.

The database must be right

For start-ups, problems with data access are critical to their business success. A uniform, modern data architecture enables the democratization of data access and dissolves data silos. The advantage is obvious, fast data access enables transparency towards customers and supervisory authorities. It also increases employee trust and creates a sense of cohesion.

Nico Gaviola is VP Digital Natives & Emerging Enterprise at Databricks EMEA. © Databricks

Governance is also crucial when using AI agents. The pressure to comply with regulations should therefore be seen as an advantage.

The triad of data provenance, versioning and automated evaluation of results helps young companies to build governance on a solid foundation. Your teams get direct visibility into how AI agents are behaving, what data they are based on and how they have changed results over time. Assessments help AI agents become more accurate to deliver the high-quality results startups need to scale their business models. This is especially important when developing proprietary AI models and moving them from the testing phase to production, while complying with legal regulations such as the GDPR or the EU AI Act.

Parloa, a German start-up with a valuation of three billion US dollars, is a good example of what this approach can look like in practice: The company has made AI agents the core of its customer service while building a unified, controlled database that is fully compliant with the GDPR and EU AI law. Its platform follows the principles of "privacy by design" so that sensitive customer data can be used without loss of control. By managing the entire lifecycle of AI agents, Parloa turns governance into something tangible, giving teams clarity on what data has been used, how agents have behaved and how outcomes have changed over time. This combination of modern architecture and strong governance enables Parloa's customers to gain access to high-quality data, increase transparency for regulators and end users, and still scale AI-driven customer interactions in environments where error or misuse is unacceptable.

Conclusion

AI agents offer European startups a unique opportunity to grow rapidly while attracting investors who are known to want to invest their money in companies that value data management, accuracy, quality and the creation of real value through technology. However, it is a mistake to rush into agent adoption without careful consideration. Start-ups that deploy AI agents without first building a unified database and ensuring sound management and evaluation risk adding more complexity than value. Ultimately, the successful start-ups will be those that can scale their business models across industries and countries, and the use of AI agents already plays an important role in this.

Nico Gaviola is VP Digital Natives & Emerging Enterprise at Databricks EMEA.