AI agents: The end of human customer service – or its savior?

The technical architecture of Agentic AI is the foundation of its impressive capabilities. Behind the scenes, a complex system is at work, consisting of various components and controlled by a clear process logic. To understand how this technology works, we need to take a look at its basic building blocks and how they interact.

Basics: LLMs, goals and actions

Large Language Models (LLMs) are at the heart of every Agentic AI. These models enable the AI to understand natural language and, based on this, to think logically. The functionality follows a structured four-stage process:

1. Information procurement: Agentic AI obtains data from various sources or from its environment, analyzes it and extracts relevant information.

2. Logical thinking/planning: In this step, Agentic AI uses LLMs to create solution strategies for the problem at hand and derives the necessary tasks from them.

3. Acting: Agentic AI carries out corresponding activities according to its planning by interacting with its environment, external tools or applications via interfaces.

4. Feedback/learning: The results of actions are fed back into Agentic AI, allowing Agentic AI to optimize and adapt itself.

The cognitive cycle of perceiving, thinking, acting and learning fundamentally distinguishes Agentic AI from traditional automation systems. While traditional chatbots operate within a fixed framework, Agentic AI enables a dynamic, context-aware and adaptive service experience. As a result, Agentic AI works in a goal-oriented manner – it receives a task and then decides independently how to solve it most efficiently.

Interaction with company systems

The true strength of Agentic AI lies in its ability to interact seamlessly with existing enterprise systems. To handle complex tasks, Agentic AI accesses a variety of data sources across the enterprise – from customer relationship management (CRM) and enterprise resource planning (ERP) to supply chain management and HR tools – and in advanced implementations, a central manager subagent works as a coordinator, breaking down a workflow into executable tasks and delegating them to specialized subagents. These sub-agents have specific domain knowledge and access to relevant systems, enabling them to perform their assigned tasks efficiently.

Integration typically takes place via APIs and specialized frameworks that ensure smooth communication between the AI and the company systems. A robust data structure is particularly important here: Agentic AI requires access to data that is organized, prepared for consumption and available in real time from distributed enterprise systems.

Risks and challenges

Agentic AI will fundamentally change customer service. This technology goes far beyond traditional chatbots and offers decisive competitive advantages through its ability to act autonomously and make complex decisions. There is no doubt that companies will benefit from significant cost savings, improved scalability and personalized customer interactions while being available around the clock. The economic benefits of Agentic AI are correspondingly impressive. Initial companies are reporting a 60 to 70 percent increase in process efficiency. These efficiency gains lead to significant cost savings through reduced processing times and reduced staffing levels.

But this advanced technology comes with significant risks. The risk of wrong decisions, data breaches and uncontrolled behavior requires robust security measures and a well thought-out governance framework. The autonomous nature of these systems requires specific security precautions that go far beyond traditional protection mechanisms and always involve humans as the central control authority. Humans must therefore remain the central control authority, supported by clear governance guidelines and continuous monitoring.