Oracle Agentic AI Foundations: Get skilled for the Agentic AI Era | oracleuniversity
By the end of the course, you will be able to:
The course is organized into six modules that build on each other from first principles to enterprise deployment.
Module 1: Introduction to AI Agents
The mental model for everything that follows: an LLM-based agent is an LLM plus tools plus a loop. We cover what makes an agent goal-directed, autonomous, tool-using, and iterative; the core reasoning patterns (Chain-of-Thought and ReAct); a walkthrough of your first agent; and a layered, defense-in-depth approach to safety and guardrails.
Module 2: LangChain for AI Agents
This module introduces LangChain and the LangChain Expression Language (LCEL). You’ll build your first agent, then go under the hood to see what a single agent.invoke() call is really doing: building tool schemas, parsing tool calls, executing functions, and deciding whether another model call is needed. That’s the understanding you need to debug agents and move them to production.
Module 3: Introduction to MCP
The Model Context Protocol (MCP) gives agents a standard way to connect to tools, data, and prompts. We cover the MCP architecture and core components, then add an MCP server to your agent starting with a simple local math server and moving to a real-world OCI Usage MCP server. The takeaway: MCP decouples agents from tool implementations, enabling interoperability, discovery, and reuse at scale.
Module 4: OpenAI Responses API and Agents SDK
This module covers the OpenAI Agent stack and how to choose between its pieces – the Responses API for simpler, single-call use, and the Agents SDK for multi-step logic, multiple agents, guardrails, and tracing. We cover tools and function calling, multi-agent systems and handoffs, and safety, then put it together in a multi-agent customer-support system that routes requests to specialized agents.
Module 5: Agentic AI for Enterprises
Building an agent is one thing; running it reliably is another. This module introduces the OCI Enterprise AI platform and OCI Enterprise AI Agents, and the division of labor: you focus on the agent’s instructions, tools, knowledge bases, and outcome, while OCI handles hosted endpoints, scaling, memory and sessions, sandboxed tools, logging, and integrations. We finish with a discussion on how to build agents using OCI Enterprise AI Platform.
Module 6: Agentic AI for Oracle AI Database
This module focuses on bringing agents to your data. We cover Oracle AI Vector Search and its workflow, the Oracle AI Database Private Agent Factory, the Select AI Agent for building agents that live inside the database, and the Oracle Autonomous AI Database MCP Server – showing how agentic capabilities can run close to your data, governed by the security you already rely on.
