Chatbots and AI Agents compared
From “Alexa, what's the weather like today?” to “Where's my package?” in an online shop – most of us have interacted with a chatbot at some point. They are primarily designed to answer simple questions. Chatbots usually work according to fixed rules within a clearly defined framework and focus on dialogue.
It becomes more complicated when multiple systems are involved, such as in a returns procedure. This is where AI agents come into play: AI agents are autonomous and goal-oriented systems that act proactively and take context into account (reasoning). To do this, they use modern technologies such as Large Language Models (LLMs) and Natural Language Processing (NLP).
For quick comparison, we have compiled the key differences between chatbots and AI agents below:
When are chatbots sufficient, and when are AI agents worthwhile?
In practice, chatbots and AI agents have long been used across a range of industries. These technologies are particularly interesting for customer service, as automation significantly reduces the workload for support teams and can improve customer satisfaction in a scalable way. But in which cases are chatbots sufficient, and when does it make sense to go one step further and use AI agents?
In which cases are chatbots suitable?
Chatbots are particularly suitable for routine questions that require a simple query in the backend: in other words, anything that can be answered quickly without involving multiple systems or complex decisions. These include questions about shipping, return periods or opening hours.
With moinAI, all important information can be stored in the Knowledge Base so that the chatbot can access it.
In short: Chatbots are useful when...
- there are many standard questions.
- the proportion of system actions is low.
In which cases are AI Agents suitable?
When it comes to complex queries, chatbots reach their limits. AI agents are particularly useful when multiple systems are involved or when resolving the issue requires multi-step processes. Typical use cases include the online returns process (warehouse check, label creation, ERP booking), suspending a subscription (billing API, email confirmation) or proactively identifying and compensating for order errors.
AI agents are therefore worthwhile if...
- many systems are involved.
- the goal is to close cases.
Interaction between chatbots and AI agents
In reality, use cases cannot be so easily delineated. Sometimes there are simple standard questions, sometimes complex queries that require action. Therefore, in most cases, a hybrid solution combining chatbots and AI agents is the best approach. It enables the two technologies to work together perfectly. A hybrid solution could look like this: The chatbot takes care of the initial contact. Simple questions requiring few system actions can thus be handled efficiently. As soon as the query becomes more complex and the chatbot's functionality is no longer sufficient, an AI agent takes over and performs the necessary action. The switch from chatbot to AI agent is seamless and takes place in the background. For the user, the interaction between the chatbot and AI agent is a coherent conversation without changing conversation partners.
Architecture
If you take a closer look at the architectures of the two technologies, it becomes clear how differently chatbots and AI agents work:
- The chatbot follows a clear line: it uses Natural Language Processing (NLP) to recognise an intent – ultimately, the user receives an answer or is forwarded to the appropriate place: NLP → intent → answer or forwarding.
- The AI agent, on the other hand, works like a small orchestrator: it plans steps, calls up external tools or APIs, draws on memory and context, checks security policies and, if necessary, involves humans in the process: Planner/Reasoner ↔ Tool orchestration (API/RPA) ↔ Memory/Context ↔ Policy Guardrails ↔ Human-in-the-Loop.
Security, control and compliance
When it comes to governance, AI agents generally require more effort than chatbots. While chatbots are limited to defined rules and processes and are therefore relatively easy to control, AI agents can independently access large amounts of data to trigger actions in external systems – this must be monitored and controlled. This increases the need for clear guardrails: Who is allowed to do what? Which steps require human approval? How can processes be tracked seamlessly?
How automation affects KPIs
Automation is particularly worthwhile when it not only simplifies tasks but also delivers measurable improvements. Here, too, chatbots and AI agents differ:
Chatbots offer an easy way to get started. They are inexpensive, can be deployed with little effort, and enable quick wins by intercepting enquiries. However, their influence on more complex business processes remains limited.
AI agents are more complex to implement, but can have a significant impact on key performance indicators.
AI agents affect the following KPIs, for example:
↗️ Tickets issued per hour
↘️ Customer Effort Score (CES)
↘️ Costs per solution
↘️ Waiting time for enquirers
Chatbots can also have a noticeable impact on these metrics. Automating simple processes often makes a big difference.
If you want to cover additional use cases and make more complex tasks more efficient, you can further expand the positive impact on key KPIs by using AI agents.
Conclusion
Chatbots retain their right to exist and have their own unique strengths: they are easy to implement, generate quick returns and simplify governance issues. They can answer simple questions in a targeted manner. Chatbots such as those from moinAI are widely used by companies that want to automate their customer communications in a scalable and efficient manner.
AI agents score points with their autonomy and ability to act. They make informed and context-related decisions, thereby positively influencing important target variables. Although implementation is more complex, with the support of moinAI's CSM team, the individual steps can be implemented smoothly, enabling quick results.
Ultimately, the choice between a chatbot, AI agent or hybrid solution depends on individual needs. In almost every case, however, it is worth using a chatbot to handle repetitive enquiries and thus generate quick profits. If necessary, AI agents can be implemented over time to solve increasingly complex enquiries and thus meet rising customer demands.
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