One of the most striking use cases of the recent developments in AI is systems that can interact with humans in a meaningful and truly helpful way.
Nowadays, the label ‘chatbot’ is often associated with simple automated bots, whereas the use of ‘AI agent’ is used to encapsulate a more encompassing role where AI can step in to accomplish tasks previously done by humans. AI technology has moved on from that which powered basic chatbots, with large, small and customised language models now forming the foundation of the system.
Also, AI agents can handle a broader range of tasks and offer personalised assistance. This makes them significantly more versatile and effective than their predecessors, enabling them to provide a rich user experience.
Simply put, an AI agent (for more, see What is an AI Agent?) is an intelligent digital assistant and communicator built using language models and natural language understanding (NLU) that understands what’s being asked and takes the required actions – all without direct intervention from humans.
Other terms that are sometimes used to describe similar AI systems are ‘intelligent assistant’, ‘AI assistant’, ‘virtual or digital assistant’ and ‘co-pilot’. Strictly speaking, there are subtle differences between these concepts, but the idea is the same – AI can now communicate with humans, help them work better, and even do the work autonomously.
When used to interact with customers, AI agents are leaps and bounds ahead of traditional chatbots. They deliver better understanding of context and nuance, more natural and fluid conversations, adaptability to handle diverse queries, the ability to multitask and can assist users more efficiently.
Areas where AI agents excel over older chatbots include:
Autonomy and Decision-Making
AI agents can have conversations and make decisions by themselves. Traditional chatbots follow strict scripts and dialogue flows, while AI agents make decisions based on the conversation, making the customer interaction more natural and useful.
Entity Extraction
Entities are key pieces of information that can be drawn from unstructured language, e.g. extracting an amount in a payment plan conversation with a customer.
Intent Recognition
This is the ability for the AI to understand what a person, e.g. a customer, is saying or wants to do. This makes achieving the best outcome a much less frustrating and more accurate process than with the old chatbots.
Emotion Detection
With AI’s ability to understand the sentiment and intent behind words, the emotion of the person communicating can be detected: angry, anxious, happy, etc.
Propensity Guidance
The AI uses intent recognition to predict the most likely outcome of a conversation and therefore make response and routing decisions based on this data.
Incorporating Generative AI
GenAI can be used in certain controlled situations such as for generating conversation summaries for human contact centre agents.
Outcomes Driven
Instead of just giving rote answers, AI agents work towards specific goals, like answering an account balance enquiry.
Adaptability and Continuous Learning
These AI agents can learn from past conversations, which makes them grow in understanding and able to handle queries in a better way.
Understanding Multimodal and Rich Media
Advanced AI agents can understand and respond using different methods such as understanding WhatsApp voice notes and reading scanned documents.
| Features | AI Agents | Traditional Chatbots |
| Understanding User Intent | Utilise advanced language models and natural language processing (NLP) to understand context and nuance | Rely on predefined keywords and scripted responses |
| Response Quality | Provide dynamic, context-aware answers that can adapt to complex queries | Offer limited, often generic responses based on static rules |
| Learning and Adaptation | Continuously learn and improve from interactions using machine learning | Do not learn from interactions; require manual updates to improve |
| Handling Complex Queries | Can handle multi-step and complex queries efficiently | Struggle with multi-step interactions, but good for simple customer interactions |
| Personalisation | Can personalise responses based on user history and preferences | Offer minimal personalisation, usually limited to basic information |
| Scalability | Highly scalable, capable of handling thousands of interactions simultaneously | Limited scalability, performance degrades with high traffic |
| User Experience | Provide a more human-like, conversational and engaging interaction experience | Offer a more systematic and scripted interaction experience |
| Cost Efficiency | Higher initial cost but lower ongoing costs as automation increases resulting in fewer human agents | Lower initial cost but higher human agent costs due to less automation |
| Example Use Cases | Customer service and engagement, technical support, personalised shopping assistants | Simple FAQs, basic customer support queries |
Overall, AI agents mark a major milestone in AI for customer engagement, using autonomy, goal-orientation, and continuous learning to engage users in more intelligent and productive conversations.
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