Intelligent Agents (IA)

Intelligent Agents use conversational systems to maintain a dialog with people or other agents that manage their activities. Conversational systems are technologies designed to engage with humans through language, offering responses that simulate a natural conversation. They are powered by artificial intelligence (AI), primarily utilizing Large Language Models to understand, interpret, and generate human language.

Intelligent agents have the ability to think about the information they are presented with and respond intelligently. They can plan out a conversational dialog based on goals and intents. They maintain internal goals and can anticipate the goals of conversation participants. IAs  have access to APIs and can initiate actions. They have internal agendas that are goal driven and continuously work to achieve those goals.

They can process both text and spoken language inputs through recognition technologies, such as automatic speech recognition (ASR) for voice and text parsing for written text. Large Language Models are used for understanding language and converting to a deeper semantic understanding. Once the meaning of input is understood an Intelligent AI can plan out a response and execute the planned steps. Steps may involve directly responding with text or images. Other actions include sending messages, searching the internet, generating content, answering questions, teaching, solving problems and much more. Most things humans can do using keyboards can now be done by Intelligent Agents.

Intelligent agents use Large Language Models to decipher intent and context behind someone's input, which may involve analyzing semantics, syntax, and pragmatics within the language. User input is processed 

This involves maintaining the context of a conversation, managing the flow, and determining the next steps in the interaction, ensuring the conversation progresses logically.

Conversational systems use natural language generation (NLG) to craft responses that are appropriate and coherent within the context of the conversation.

Many conversational systems have the capacity to learn from interactions to improve their performance over time, thanks to machine learning algorithms.

Some systems can support multiple modes of communication, like text, voice, and sometimes even visual elements or gestures.

These systems can often be integrated into various platforms (like messaging apps, websites, or devices) and can be scaled to handle varying loads of interactions.