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In the digital world, technology is evolving almost before our eyes, yet few areas have undergone as dramatic a transformation as customer support automation. What began as interactive guides has, thanks to the rise of large language models (LLMs), reached a point where digital systems take over entire agendas previously reserved exclusively for humans. This evolution from simple FAQ chatbots to advanced AI agents is not just a technological detail. It is a fundamental shift in how companies communicate with their customers—and how efficiently they can operate.
The first chapter of this story was written by FAQ chatbots. Most were built on rigid rules and decision trees. Users navigated them through clickable buttons and clearly defined branches. These bots essentially functioned as interactive signposts and were great for very simple and predictable questions, such as checking opening hours or finding a branch address. They were a useful tool for filtering basic inquiries and staying in touch with customers even outside business hours.
The breakthrough came with the second phase, defined by AI chatbots equipped with natural language processing (NLP). This generation no longer needed guiding buttons. A customer could ask a question in their own words, use slang, or make a typo—and the system could still identify the intent. AI chatbots began working with extensive information sources and documentation, enabling them to handle a much wider range of inquiries. Despite their intelligence, however, they still remained in the role of informants. Their primary function was to “answer.” They could explain how to file a complaint, but they could not actually create it in an internal system.
Today, we are in the era of AI agents powered by LLMs (Large Language Models). Unlike previous technologies, LLMs can reason in context. That means an AI agent no longer just searches for keyword matches in a database—it truly understands the task it has been given.
Thanks to large language models (LLMs), AI agents’ capabilities are moving to an entirely new level. These models act like an intelligent brain that can plan individual steps and break down even complex requests—such as a complete damaged-goods claim process—into logical sub-tasks.
An LLM works like a brain that gives instructions to your systems. It can easily connect to an e-commerce platform or a warehouse. It also speaks to customers in a human, empathetic way, so it no longer sounds like a robot but communicates in line with your brand.
While earlier systems solved a “question,” an LLM-powered AI agent understands a “task.” If a customer needs to change a delivery date, the agent can find the order, verify carrier capacity in real time, update the database, and send a confirmation—autonomously and without human assistance.
For companies, this evolution represents a major shift in mindset. It’s no longer about “automating answers,” but about truly automating processes. The most important task for modern customer care managers is no longer compiling lists of questions and answers, but mapping company processes. The goal is to identify routine actions that unnecessarily waste employees’ time and then delegate them to AI agents. The result is not only a dramatic increase in efficiency and 24/7 service availability, but above all freeing up human operators—so they can focus on creatively solving the most complex customer cases.
LLMs (Large Language Models) are technologies (ChatGPT, Gemini, Claude) that serve as the “brain” of an AI agent. They enable the agent to understand the context of human language, reason logically, and decide which tool or system to use at a given moment to complete a task.
Yes—when properly configured (e.g., within the coworkers.ai platform), all communication is encrypted and compliant with GDPR. AI agents access only the data you grant them permission to use via secure API interfaces.
AI agents are designed to know their limits. If they encounter a request outside their capabilities—or a customer who, for any reason, needs human assistance—they hand the inquiry or task over to a human operator. They also provide a summary of the conversation so far, so the customer doesn’t have to repeat anything.
