AI agents are quickly becoming part of how software gets built. Instead of responding to a single prompt, an agent works toward a goal: it reasons about what to do next, calls tools and APIs, checks the result, and decides whether to keep going. That shift from chat to action is changing what developers, data scientists, and engineers need to know.

To help individuals and organizations build these skills, we are pleased to announce the Oracle Agentic AI Foundations course and certification.

Even better, the certification exam is free.

Start with the Foundations

The Agentic AI Foundations course is built for learners who want to understand how agents actually work and how to build them. It’s a good fit if you are:

  • An Agentic AI beginner looking for a structured, hands-on starting point.
  • An AI/ML engineer moving from models to agentic systems.
  • A cloud developer adding agents to your applications.
  • A data scientist grounding agents on enterprise data.

Going in, the course assumes basic familiarity with large language model (LLM) concepts, working knowledge of Python, and basic familiarity with Oracle Cloud Infrastructure (OCI).

If you’re newer to AI and ML concepts, the OCI AI Foundations course is a good place to begin before diving in here.

What You’ll Learn

By the end of the course, you will be able to:

  • Understand core AI agent concepts.
  • Design AI agents using LangChain and the OpenAI Agent stack.
  • Implement Model Context Protocol (MCP) concepts.
  • Build agents using the OCI Enterprise AI platform.
  • Apply Oracle AI Database capabilities for agentic AI.

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.

Take a Quick Tour

Want a fast look at what’s inside before you start? The rapid-fire course tour walks through all six modules in under three minutes – a visual flip book of the full coursework so you can see the scope and flow at a glance.

Earn the Oracle Agentic AI Foundations Certification

The learning path includes a free Foundations Associate certification exam so you can validate your skills. The exam assesses the competencies covered across the six modules: agent fundamentals and reasoning patterns, building agents with LangChain and the OpenAI Agent stack, implementing MCP, building on the OCI Enterprise AI platform, and applying Oracle AI Database for agentic AI.

The exam consists of 40 questions to be answered within 60 minutes, with a passing score of 65%. To help you prepare, the course includes skill checks throughout, exam prep material, and a free practice exam you can take before sitting for the certification.

Get Started

The free certification exam means there’s nothing standing between you and getting started.

So, here’s our ask: take the course, earn the free certification, and share your achievement on LinkedIn – tag Oracle University – so we can celebrate with you. Then put the skills to work in your day-to-day role, on real problems and real projects.

The best way to learn agentic AI is to build with it. Go build!