Agents

Humans are capable of complex reasoning. When posed with a problem, they break it up into smaller steps, iteratively going through each step, learning and solving to reach the end goal. Most AI models up until now havent been capable of this complex reasoning tasks that require multi-step thinking and adaptive learning. Last month OpenAI released o1 which addresses these challenges by incorporating Chain-of-Thought and Reinforcement Learning to achieve near-human reasoning capabilities.

Oracle among other companies announced recently that 50+ role-based AI agents within the Oracle Fusion Cloud Applications Suite will help successfully execute frequent, repetitive tasks. Other companies are doing the same. In this article I will discuss what AI agents are, what are some of the use cases and link some tools/frameworks that can help you design and build agents.

RAG is one of the most common use cases that has been implemented in the past couple of years. Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of LLMs by combining them with external knowledge sources. It involves retrieving relevant information from a knowledge base, incorporating it into the LLM's context, and then generating a response that leverages both the LLM's internal knowledge and the retrieved information. Building RAG applications requires integrating various components like vector databases and search algorithms, which can be quite involved. In this blog we'll briefly talk about RAG basics and leveraging OpenAI's assistants to build simple RAG applications.