From AI assistants to agents - how companies can benefit from the next stage of AI evolution

From assistant to agent: What's behind it all?

While conventional AI assistants such as chatbots, voice control systems or coding aids react to user instructions, AI agents take an active approach. They have comprehensive planning capabilities, work independently with various data sources, APIs and tools and coordinate complex processes, e.g. from purchasing and reporting to customer interaction. In practice, this means that an agent can complete a task not just on demand, but completely independently. This also includes intermediate steps, queries and optimisations.

The technological drivers of this development are multimodal models (such as GPT-4o or Gemini 2.5), new integration protocols (such as the Model Context Protocol) and specialised frameworks. The latter enable the secure and efficient integration of agents into existing system landscapes.

Potential benefits for companies

The potential is enormous. Companies benefit from

• Process automation across departmental boundaries (e.g. in sales, purchasing, HR or IT support)
• Cost reduction through efficiency gains
• Faster decision-making thanks to continuous data analysis
• Individualised customer interaction in real time - even across different channels
   

Early investment in AI agents can also secure competitive advantages, for example through optimised supply chains, improved forecasting or more agile responsiveness in day-to-day business.

Potential challenges and risks for companies

However, the new autonomy also brings challenges:

• Security and control: agents must act reliably and transparently. Uncontrolled actions or incorrect decisions can cause real damage.
• Quality and reliability: Many agents are still susceptible to misinterpretation or so-called hallucinations in complex contexts.
• Ethics and regulation: The legal framework for decisions made by autonomous systems has not yet been conclusively defined in many countries.
• Acceptance and culture: The introduction requires not only technology, but also education and trust among employees and customers.

Grieshaber's approach to this development

At the Grieshaber Group pursues a practice-orientated and structured approach in order to develop the potential of agent-based AI in a targeted manner. The focus here is on identifying specific areas of application with measurable added value, in which the AI agents are initially trialled in clearly defined pilot projects. These initial steps make it possible to test technology and processes in real operations without jeopardising existing structures.

At the same time, great importance is attached to IT security and clean governance. This means that AI agents are only given controlled access to defined systems. In this context, the IT security and innovation departments work closely together to recognise risks at an early stage and proactively meet regulatory requirements.

Another success factor is the involvement of our employees. The Grieshaber Group provides transparent information about the goals and functionality of new technologies, promotes digital skills in a targeted manner and thus creates a climate of openness and participation. After all, technological change is only possible with a culture that sees change as an opportunity.  

The technical architecture is deliberately modular. Instead of comprehensive complete solutions, the focus is on scalable components that can be flexibly connected to existing systems. This can be done via secure API interfaces or middleware solutions, for example. Dadu