Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
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72% of companies deploying AI solutions.1 Oracle leverages AI agents combining large language models, natural language processing, and retrieval-augmented generation (RAG).
Explore Oracle AI agents, their use cases, and benefits:
OCI generative AI agents is a fully managed service that integrates large language models (LLMs) with an intelligent retrieval system. It is designed to provide relevant answers by searching a specified knowledge base.
Oracle now offers more than 600 AI agents within its Fusion Cloud Applications Suite, alongside over 100 certified partner agents in the Fusion AI Agent Marketplace. The company launched 22 new Fusion Agentic Applications built from teams of AI agents integrated into Oracle Fusion Cloud Applications for HR, finance, supply chain, and customer experience.2
These AI agents are built to handle multi-step processes, adapt to new situations, and respond to natural language prompts, offering greater flexibility and precision compared to earlier rule-based systems. OCI generative AI Agents offers multiple methods for onboarding data, enabling users and their customers to interact with the data through a chat interface or an API.
Oracle AI Agents operate retrieval and response systems as orchestrators capable of planning, coordinating, and executing complex workflows across enterprise tools and data sources.
Unlike single-function chat-driven AI, agentic orchestration enables models to:
This orchestration layer allows Oracle AI Agents to move beyond question-answering and into process execution.
For deeper context on orchestration, explore:
As agents expand beyond internal systems, web execution environments and interoperability standards are becoming critical:
There are two different ways you can provide data for OCI Generative AI Agents to use as a knowledge base. A knowledge base is essentially the collection of information or documents that the AI agent searches to generate answers. Here’s a breakdown of what each type means:
This option allows you to store your data in Oracle Cloud Infrastructure (OCI) Object Storage. With this approach, you can:
This option allows you to use your existing systems to host and manage the data the AI agent will access. Oracle provides integration with specific tools for this purpose. It is available on systems like:
This option gives you flexibility and control if you have an infrastructure for storing and managing data. You can integrate the AI agent into your existing systems without migrating data to OCI Object Storage.
Retrieval-augmented generation (RAG) agents, such as agentic RAG, combine retrieval and language generation capabilities to produce accurate and context-aware responses. The agent retrieves relevant documents or data and generates coherent answers based on this information.
Example use cases include:
Explore general Agentic AI use cases with some real-life examples.
Oracle has introduced the AI Agent Studio, a centralized environment to simplify the deployment and monitoring of AI agents. This studio represents a move toward democratizing AI within the enterprise through two key pillars:
While many agents focus on information retrieval, the Studio allows for the creation of Workflow Agents. These are distinct from standard RAG agents because they:
The OCI Generative AI RAG Agent was officially released on September 25, 2024, after a beta version was previously announced. The new RAG (Retrieval-Augmented Generation) Agents introduced several enhancements compared to the existing OCI Generative AI Agents:
Oracle employs a variety of AI technologies:
Embedded generative AI in business applications: Integrates generative AI in Oracle Cloud Applications for insights.
OCI generative AI: Offers Cohere and Meta models in a managed environment with fine-tuning and API-based integration.
OCI generative AI agents: Combines large language models (LLMs) with retrieval-augmented generation (RAG) for accurate, enterprise-specific responses.
Oracle code assist: Provides an AI code companion for Java, SuiteScript, PL/SQL, and OCI development.
OCI data science: Supports building, training, and managing custom LLMs with tools like Hugging Face Transformers and PyTorch.
OCI AI infrastructure: Delivers high-performance compute resources with NVIDIA GPU-powered instances for LLM workloads.
AI vector search in Oracle database 23c AI: Enhances search with AI vectors for precise results.
HeatWave GenAI: Offers in-database LLMs and vector storage without requiring additional expertise or costs.
Autonomous database select AI: Uses LLMs to process natural language queries and generate Oracle SQL.
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