Classic chatbots: full control over conversational flow

Classic chatbots are widely used on websites and mobile applications, serving as the first point of contact between businesses and their customers. These bots operate on pre-defined conversation trees that are carefully crafted to ensure precise control over the flow of interactions. This approach ensures that the chatbot responds consistently, aligns with brand messaging and handles specific use cases effectively.

In addition to rule-based logic, classic chatbots often use predictive AI in the form of Natural Language Understanding (NLU) and Natural Language Processing (NLP). These technologies enable the chatbot to understand the user’s intents, analyse linguistic patterns and deliver more contextually appropriate responses, improving the overall user experience.

Different types of classic chatbots include:

• Navigational chatbots: Designed to guide users through websites or apps, helping them find information quickly and efficiently.
• Smart handoff chatbots: Rule-based interactions designed to gather information before handing off to a human agent to handle more complex queries.
• Integrated chatbots: Seamlessly connected to external systems (e.g. customer digital portals, mobile apps, CRM, databases) to provide dynamic, context-aware responses.

While classic chatbots excel in controlled environments, adding artificial intelligence can significantly enhance their capabilities. Depending on the desired level of AI integration, we can transform these bots into AI Agents, incorporating features such as:

• Generative fall-back: When rule-based responses fall short, AI generates contextually relevant responses to maintain the flow of the conversation.
• Generators: AI-driven modules can create dynamic content, from personalised recommendations to complex data summaries.
• Knowledge-based Agents: Using AI to fetch, analyse and interpret your public or private content, such as websites, internal documents and more. After indexing this information, the agent can effectively respond to queries and engage in meaningful conversations about the content. Simply provide the content and the agent does the rest.
• Advanced AI Agents: Applications built on cloud platforms that provide a new way to create virtual agents using LLMs. You only need to provide natural language instructions and structured data. This can significantly reduce the virtual agent creation and maintenance time and enable brand new types of conversational experiences for your business.

Understanding the phases of artificial intelligence is crucial for businesses looking to adopt AI-driven solutions, as well as for understanding how different types of chatbots work.

• AI Agents: These systems augment traditional chatbots with AI capabilities, enhancing their ability to handle diverse queries while still relying on human-defined parameters. Advanced AI Agents, as discussed earlier, are prime examples of this foundational phase, setting the stage for more autonomous AI capabilities.
• Agentic AI: Representing a significant leap from AI Agents, Agentic AI systems have a higher degree of autonomy. They don’t just react to input, but proactively make decisions, learn from interactions and optimise their performance over time. This demands more than just pattern recognition. It requires decision-making facilitated by reasoning engines that can evaluate multiple variables, draw logical conclusions and adapt their responses based on contextual understanding. It reinforces the idea of chain-of-thoughts, where AI can break down complex tasks into a series of manageable steps, similar to how a human might approach problem solving. This structured reasoning process enables Agentic AI to handle sophisticated tasks that require critical thinking and adaptive learning. Salesforce Agentforce is a prime example, demonstrating how Agentic AI can revolutionise customer service by delivering personalised, context-aware experiences with minimal human intervention.
• Autonomous AI: The pinnacle of AI development, autonomous systems operate independently, making complex decisions without human oversight. These systems are capable of self-learning, adapting to new environments and performing tasks that have traditionally required human intervention.