Natural Language Processing (NLP)

NLP (Natural Language Processing)

Natural Language Processing is a branch of artificial intelligence (AI) that enables machines to understand, interpret, and respond meaningfully to human language, whether written or spoken. The goal is to make language processing by machines as natural as possible – similar to how humans do it.

In a business context, NLP supports tasks such as analyzing emails, evaluating customer feedback, and automatically processing unstructured text. Information is extracted from the language and converted into structured data that can then be used in other processes.

Typical tasks of NLP:

• Text classification: Automatic categorization of texts, e.g., recognition of spam emails
  
• Speech recognition: Conversion of spoken language into text (speech-to-text)
  
• Speech synthesis: Generating spoken language from text (text-to-speech)
  
• Named entity recognition (NER): Identifying names, places, dates, etc. in texts
  
• Machine translation: Automatically translating texts into other languages
  
• Sentiment analysis: Recognizing emotions and opinions in texts
  

Challenges:

Processing natural language is particularly difficult for machines because human language is very complex and context-dependent. Words can have different meanings depending on the circumstances. In addition, there are dialects, colloquial language, irony, ambiguities, and even typos—all of which an NLP system must be able to classify correctly.

Relevance to Agentic AI:

In Agentic systems, NLP forms the basis for AI agents to respond independently to linguistic input. For example, they recognize customer concerns, automatically initiate appropriate processes, inform relevant contact persons, and accompany the processing – without manual intervention.

A customer writes an email or chat message:

“The system hasn't been working properly since the last update.”

Step 1: Language understanding through NLP

The text is automatically analyzed using natural language processing. The AI recognizes:

• This is a complaint or support request.
  
• The cause appears to be related to a recent software update.
  
• The customer describes a functional problem without providing technical details.
  
• The tone is slightly negative but factual.
  

The NLP models extract important information such as:

• Key terms: “update”, “doesn't work”, “system”
  
• Time (implicit: since the last update)
  
• Problem type: malfunction after change
  

Step 2: Ability to act through Agentic AI

Instead of just understanding, the AI continues to act independently – it becomes an agent that makes decisions and sets processes in motion:

1. Automatic creation of a support ticket
2. The AI agent formulates a clearly structured ticket with a summary of the problem, customer data, technical context (e.g., version of the last update) and sends it to the internal system.
   
3. Routing to the responsible team
4. Based on the type of problem, the agent recognizes that, for example, the backend team is responsible and automatically forwards the ticket there.
   
5. Communication with the team
6. The responsible team is actively informed – for example, via internal tools such as Jira, Slack, or email – with all relevant information attached.
   
7. Monitoring the processing status
8. The agent tracks in real time whether the ticket is being processed, whether there are any queries, and how progress is developing. If there are any delays, the AI can escalate or follow up.
   
9. Proactive feedback to the customer
10. Even before the customer inquires, they automatically receive a message such as: “Your request has been forwarded. We are working on a solution and will keep you informed.”
    
11. Completion and feedback
12. Once the problem has been resolved, the agent informs the customer again and asks for feedback to further improve the service.

Interaction between NLP and Agentic AI

• NLP: Recognizes and understands the meaning of the customer's text
  
• Agentic AI: Makes decisions independently, initiates measures, and accompanies the entire process until a solution is found

Thanks to the combination of NLP and Agentic AI, a company can not only automate customer support, but also improve it significantly: Response times are reduced, solution times are shortened, and the customer feels that they are being taken seriously right away – without any human intervention in the initial phase.