Conversations between businesses and customers have changed. Many organisations have experimented with chatbots to handle basic enquiries or automate parts of the customer journey with mixed results. What once relied on simple scripted chatbots has now evolved into intelligent AI agents that can understand intent, interpret emotion, and deliver outcomes, not just answers.

While both chatbots and AI agents are designed to automate conversations, the difference between them is significant. One follows instructions; the other understands context and adapts.

In this post, we’ll look at  what really sets AI agents apart from traditional chatbots, why that evolution matters for modern customer engagement, and how it’s redefining how organisations interact with their customers.

From Chatbot to AI Agent

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. 

AI Agent Definition

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. 

Using an AI Agent for Customer Engagement

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.

This table highlights the differences between advanced AI agents and traditional chatbots, particularly in terms of their capabilities, adaptability, and the quality of customer engagement they can provide.

Overall, AI agents represent a transformative leap forward in conversational technologies , not just better bots, but systems that reason, adapt, and pursue outcomes rather than merely respond.

But for businesses operating in credit, collections, and insolvency, the difference is more than technological. It’s strategic. In these sectors:

• AI agents can identify vulnerability signals early (e.g. emotional distress, reduced capacity to pay) and act accordingly.

• They can negotiate payment plans in context, dynamically adjusting terms based on real-time data and customer responses.

• They enable scaling: hundreds or thousands of conversations can be managed autonomously, bringing in human agents for more complex cases.

• They help deliver better commercial outcomes like higher recovery rates, lower cost per £ collected, fewer write-offs, while also delivering better customer experience and regulatory compliance.

If your team is still relying on legacy chatbots or rule-based automation, now is the time to reassess. Think of AI agents not as a replacement, but an evolution. One that can elevate your customer engagement, strengthen your bottom line, and reshape how collections operations are managed.

Want to see what that looks like in a real collections environment? Contact us we’d be glad to walk you through how AI agents are changing the rules of customer engagement for credit and collections teams.