Conversational agent: definition, types, and use cases

"Hello, how can I help you?" You've probably seen this phrase in a chat before. But who (or what) wrote it? A human or a chatbot?

In this guide, discover what a chatbot is, how it works, and how it can transform your customer relationship.

‍

Conversational agent: definition

A conversational agent is a computer program capable of communicating with humans in natural language. It can take the form of a text-based chatbot, a voice assistant, or an interactive avatar.

The objective: to simulate a human conversation to inform, assist, or guide the user.

‍

The different types of conversational agents

1. Rule-based chatbots

The simplest ones. They work with predefined decision trees:

• If the customer says X → respond with Y
• Keyword recognition
• Button/choice-guided tour

Advantages: predictable, easy to maintain
Limitations: rigid, do not understand language variations

2. NLP Chatbots

Use natural language processing (NLP) to understand the intent behind words:

• Semantic analysis of the message
• Extraction of entities (date, product, etc.)
• Response tailored to the detected intent

Advantages: more flexible, include reformulations
Limitations: require training, may misinterpret

3. Generative AI agents

The new generation, based on Large Language Models (LLMs) such as GPT or Claude:

• Understand the context and nuances
• Generate original and personalized responses
• Adapt to unforeseen situations

Advantages: very natural, versatile
Limitations: risk of hallucinations, require safeguards

4. Voice assistants

The audio dimension of conversational agents:

• Speech recognition (speech-to-text)
• Understanding intent
• Text-to-speech

Examples: Alexa, Google Assistant, Siri, or IVRs (interactive voice response systems) in call centers.

‍

How does a chatbot work?

The basic flow

1. Reception: the user's message is captured (text or voice)
2. Comprehension: analysis to identify intent and entities
3. Processing: business logic, database query, API call, etc.
4. Generation: response formulation
5. Feedback: display or vocalization of the answer

Technical components

• NLU (Natural Language Understanding): understanding the message
• Dialog Manager: managing the conversation flow
• NLG (Natural Language Generation): formulating the response
• Integrations: connection to third-party systems (CRM, knowledge base, etc.)

‍

Conversational agents and customer service

Customer service is the natural playground for conversational agents:

Common use cases

• Dynamic FAQ: instant answers to frequently asked questions
• Qualification: gathering information before transferring to an agent
• Order tracking: real-time status
• Appointment scheduling: automation of scheduling
• Level 1 support: resolution of simple requests

The benefits

• 24/7 availability: no waiting in line at 3 a.m.
• Instant response: no waiting
• Scalability: 1 or 1,000 simultaneous conversations
• Consistency: same response, same quality
• Release of agents: focus on complex cases

Klark is an AI chatbot specialized in customer service: it understands requests, draws on your knowledge base, and responds automatically or assists your agents in real time.

‍

Conversational agent vs. chatbot: what's the difference?

The terms are often used interchangeably, but there is a subtle difference:

A chatbot is a type of conversational agent, but not all agents are chatbots.

‍

Designing your chatbot well

1. Define the scope

What should (and should not) the agent do? Clear boundaries prevent frustration.

2. Design conversational journeys

Anticipate the different paths the conversation may take. Plan for edge cases.

3. Customize the personality

Formal or informal tone? Use "you" or "you"? The agent must reflect your brand.

4. Plan for escalation

When the agent cannot respond, the transfer to a human must be seamless and contextual.

5. Anchor to your data

Connect the agent to your knowledge base, FAQ, and business systems for accurate answers.

6. Test, measure, iterate

Analyze conversations, identify failures, continuously improve.

‍

Performance indicators

• Resolution rate: % of requests resolved without human intervention
• Transfer rate: % of escalated conversations
• Satisfaction: post-conversation user feedback
• Comprehension rate: % of correctly identified intentions
• Talk time: average duration of calls

‍

Mistakes to avoid

Mistake #1: Pretending to be human

Users don't like to be misled. Be transparent: "I am a virtual assistant."

Mistake #2: Too broad a scope

An agent who claims to do everything and often fails is worse than a limited but effective agent.

Mistake #3: No escape route

If the user wants to speak to a human, they should be able to do so easily.

Mistake #4: Ignoring the context

Repeating the same questions when the user has already provided the information is frustrating.

Mistake #5: No follow-up

Deploy and forget. A conversational agent must be continuously maintained and improved.

‍

The future of conversational agents

Trends shaping the future:

• Multimodality: text, voice, and image in the same conversation
• Proactivity: the agent anticipates needs
• Deep personalization: adaptation to profile and history
• Omnichannel integration: seamless continuity across channels
• Increased autonomy: ability to perform actions, not just inform

‍

Frequently Asked Questions

Can a chatbot replace human agents?

No, it complements them. It handles simple, repetitive requests, freeing humans to deal with complex and emotional cases.

How much does a chatbot cost?

From free (basic chatbots) to several thousand euros/month (advanced AI solutions). ROI is calculated in terms of tickets avoided.

How long does it take to deploy a chatbot?

From a few days (simple chatbot) to several months (AI agent connected to your systems).

Are technical skills required?

Modern solutions are increasingly no-code. But expertise helps in complex cases.

‍

Conclusion

Chatbots have become an essential part of modern customer service. When well designed, they improve the customer experience while reducing costs.

The keys to a successful chatbot:

• Define a clear and realistic scope
• Choose the technology that suits your needs
• Anchor your data for accurate answers
• Always plan for the escalation to the human level
• Measure and continuously improve

Ready to deploy an intelligent conversational agent? Discover how Klark can transform your customer service.