Overview

NLU Agents

Artificial intelligence is a branch of knowledge that deals with the development of intelligent computer systems, i.e., systems that exhibit characteristics that we associate with human behavioral intelligence: Language comprehension, learning, reasoning, problem solving, etc.

Natural Language Processing (NLP) is a subfield of AI that enables computers to learn, understand, and produce content in natural language. Using AI and NLP capabilities, agents can understand user utterances in natural language, infer tasks, and extract information needed to perform them successfully.

Using artificial intelligence and NLP capabilities enabled virtual assistants to understand user utterances in natural language, infer the task from the user utterance, and extract the information needed to perform the task successfully.

To effectively use and configure agents, it's important to understand the core components that enable their conversational capabilities.

Intents & Entities

Agents operate through three fundamental concepts that work together to process and respond to user interactions:

• Intents: The intent represents what the user wants to accomplish or communicate to the agent - essentially, what the user expects the agent to understand when they say something.

• Utterances: These are the actual sentences or phrases that the user says to the agent.

• Entities: These are specific keywords or data points associated with the intents that determine the agent's response, as they are necessary for executing the action identified by the intent.

intents and utterances

The conversational agent's job is to detect the intent and entities necessary to carry out a conversation from the user utterance.

Choosing the right governance

When implementing agents in your business, you'll need to select an appropriate AI governance model based on your specific use case and complexity requirements. SCAI offers different approaches to handle various interaction patterns:

The Intent-based model works best for structured, predictable interactions with predefined conversational paths - ideal for customer service scenarios with clear workflows like order tracking or basic support inquiries.

For organizations requiring more flexibility, Composite models blend intent recognition with dynamic problem-solving capabilities. In NLU-first configurations, the system prioritizes intent matching while transitioning to AI agents for complex tasks. Agentic-first configurations leverage dynamic AI capabilities while using intents for specific deterministic cases, allowing creative responses with structured routing when needed.

Learn more about Governance Types in Syntphony CAI

The Conversational agent's job is to detect the intent and entities necessary to carry a conversation from the user utterance.

Buildint intelligent agents

The intelligence of agents is not innate, but must be developed through training with machine learning, big data, and new technologies.

An agent is intelligent when it understands the user's needs, comprehends the context, and responds to the user based on their requirements, mood, and situation. This intelligence gives agents the ability to handle various conversation scenarios with ease.