תוכנית Oracle AI Week 2026

Building a Production – Grade AI Virtual Assistant

How do you evolve from a simple LLM-based chatbot into a scalable AI platform combining conversation, knowledge, speech, and real-time interaction?
Based on three years of hands-on experience designing and running the AI backend for Sidekick Platform, this session presents the architecture behind a modern Virtual Assistant and the key decisions required to operate it at scale.

Operating a Production AI Platform – Models, Prompts, Configuration and Lessons

Production AI systems quickly move beyond a single model and a few hardcoded prompts. Models change, providers evolve, prompts become business-critical assets, and the backend itself requires strong operational control, configuration, observability and administration.
This session presents practical architecture and best practices for managing models, prompts and the operational backend of a production AI platform, based on lessons learned from real-world use.

RAG in the Real World – What We Learned After Putting It into Production

RAG often looks simple in a diagram: create embeddings, search a vector database, and send the results to an LLM. In production, retrieval quality quickly becomes one of the hardest and most important parts of the AI architecture.
This session follows the practical evolution of RAG based on real production experience.

Oracle Integration Foundation for Enterprise AI Agents

How do we connect AI agents to enterprise systems and enable them to perform business actions under control?
This lecture will present Oracle Integration as the infrastructure for connecting applications, data and AI services. It will review MCP, AI Assistant and Human-in-the-Loop for creating intelligent business processes with human oversight.
Key topics include:
Connecting systems and exposing integrations as tools for AI agents via MCP
Managing access to MCP servers through MCP Gateway
Assisting in the development and documentation of integrations using AI Assistant
Incorporating human approvals and handling exceptions via Human-in-the-Loop
Demonstrating a business process that combines an AI agent, integration, and human control.

This lecture is designed for architects, integration and application developers, and technology leads with basic familiarity with APIs and system-to-system integration. No prior experience in AI agent development is required.

Participants will gain understanding of the role of Oracle Integration in connecting AI agents to existing systems and incorporating human control into business processes.etails soon...

Agentic Development with Oracle Database, APEX & MCP

Practical AI with Oracle Database, SQLcl MCP and APEX

AI-assisted development is evolving rapidly, while Oracle Database and APEX provide the stability, security, and structure required for enterprise applications.
This session explores how Oracle development teams can combine the best of both world-using AI agents to accelerate and enhance development while continuing to rely on the strengths of the Oracle Database and APEX’s declarative, low-code model.

We’ll demonstrate practical ways to integrate AI agents throughout the development lifecycle, including generating, reviewing, and testing SQL and PL/SQL, interacting with the database through SQLcl MCP, and leveraging new Oracle APEX capabilities such as application creation with APEXlang and built-in AI agents.
We’ll also show how AI can help build tailored APEX solutions using PL/SQL, HTML, CSS, and JavaScript, create rich visual and interactive components, and extend the platform beyond standard declarative features.

The focus is on practical, controlled adoption of AI-selecting the right use cases, providing the right business and technical context, integrating with Git and CI/CD workflows, and validating generated code-to accelerate development, modernization, and prototyping without compromising security, quality, maintainability, or developer control.

Oracle Data Base AI: The Big Picture

In this session we will cover major components of AI capabilities in Oracle DB.
• MCP Server
• Vectors & RAG, Vector Indexing
• Enterprise Scale AI
• AI Profile
• Comments and Annotations
• SelectAI (NL2SQL, Synthetic data, Text Summarization, Translations, External Data Gateway, AI Agents, Multi Agents Workflows…)
• Private Agent Factory
In this session we will provide an introduction. Some of the topics will be covered in deep dive sessions.

Select AI in Oracle DB

In this session we will cover major components of AI capabilities in Oracle DB.
• MCP Server
• Vectors & RAG, Vector Indexing
• Enterprise Scale AI
• AI Profile
• Comments and Annotations
• SelectAI (NL2SQL, Synthetic data, Text Summarization, Translations, External Data Gateway, AI Agents, Multi Agents Workflows…)
• Private Agent Factory
In this session we will provide an introduction. Some of the topics will be covered in deep dive sessions.

Private Agent Factory (PAF)

Private Agent Factory is a free component that can be set both as part of OCI Marketplace and as image on your local or cloud environment.
It requires access to Oracle DB and some LLM and helps user to easily create and provide access to AI agents.
It comes with easy-to-use UI for AI Agents creation, maintenance and a built-in runtime UI.
Many thousands of production Agents are running worldwide based on Private Agent Factory. In this session we will explain the basic setup of PAF and create (and run) various Agents with it.

Oracle Integration Foundation for Enterprise AI Agents

Foundation: How The Engine Executes Your Query

This session takes you from SQL text to a plan tree you can read and cost, ending on the join algorithms behind most of it.

Performance Optimization Techniques

This session is about the levers: what an index really costs, how to design one the planner will use, when partitioning pays, and the estimates that decide all of it.

AI In Performance Optimization

This session is the AI part: why a model reading only your query text guesses, what evidence turns it into an analyst, the read-only MCP tool surface, the agentic tuning loop and what an agent may and may not do to production.