AI Agents have become an essential asset for businesses aiming to optimize customer service, improve user experience, and automate tasks efficiently. Over time, they have evolved from basic rule-based systems into advanced AI-driven Agents that comprehend natural language, context, and intent. This transformation has fundamentally reshaped the way businesses engage with customers and streamline internal operations.
According to IBM, integrating AI into virtual agents can reduce the need for human intervention, potentially cutting labour costs by up to 30% on customer support services. Furthermore, chatbots can handle 80% of routine tasks and effectively resolve customer inquiries.
The Era of Rule-Based Chatbots
The first generation of chatbots operated on rule-based systems, relying on pre-defined rules and decision trees to function. These bots could only recognise specific keywords or phrases programmed into their design. Among the pioneers of this approach was ELIZA, a chatbot created in the 1960s by Joseph Weizenbaum. ELIZA mimicked a Rogerian psychotherapist by identifying keywords in user inputs and delivering scripted responses.
While these bots were limited to simple and predictable interactions, unable to understand context, learn from new data, or engage in natural conversations, they laid the groundwork for the evolution of AI-powered conversational interfaces.
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