Agent Automation

 This agent enhances a language model’s capabilities by combining generative responses with information retrieved from a database or document collection. It’s especially useful for answering questions or providing information that’s not already encoded in the model’s training data. For example, a RAG agent can be used to fetch current news articles or scientific papers to provide up-to-date answers.

This agent allows the language model to interact directly with SQL databases. It can understand and generate SQL queries based on user inputs, enabling users to retrieve, insert, update, or delete data in a database. This is particularly valuable in business settings where users need to access and manipulate structured data stored in relational databases.

An extraction agent is specialized in parsing texts and extracting specific information like key facts, figures, names, or dates. This can be extremely useful in fields like legal document analysis, where it’s important to quickly identify relevant details from large volumes of text.

This agent categorizes or labels text based on content, sentiment, or other criteria. In content management systems, a tagging agent could automatically categorize articles and posts, enhancing searchability and organization. It’s also used in sentiment analysis to label texts as positive, negative, or neutral.

By interfacing with various APIs, this agent extends the capabilities of the LLM to external services and data. For instance, it can use weather APIs to provide real-time weather updates or financial APIs to fetch the latest stock prices.