The Building AI Agents with OpenAI Specialization prepares you to design, develop, and deploy advanced AI agents using OpenAI models, AgentKit, memory systems, retrieval-augmented generation (RAG), and the Model Context Protocol (MCP). This program teaches you the same techniques used in modern AI-driven applications, personal assistants, automation systems, and multi-agent architectures.
Across three hands-on courses, you will learn how AI agents plan, reason, use tools, store and retrieve memory, communicate with other agents, and connect with external applications.
In Course 1, you’ll build foundational agent architectures, implement tool calling, configure environment variables securely, and create reasoning workflows.
Course 2 focuses on intelligent memory design—including short-term memory, long-term memory, summarization, embeddings, vector search, and hybrid RAG + memory agents. You will also integrate knowledge retrieval using Pinecone and MCP context fields.
In Course 3, you will deploy end-to-end AI assistant systems with Streamlit, create multi-agent communication flows (A2A, MCP), integrate APIs, implement personalization, and build complete cloud-ready agent applications.
By the end of the specialization, you will be equipped to build scalable, production-grade AI agents capable of reasoning, recalling information, taking actions, and collaborating with other agents—skills essential for modern AI engineering and enterprise automation.
Praktisches Lernprojekt
Learners will complete a series of hands-on projects that will guide them through the full process of building AI agents. They will create agents that can plan tasks, use short-term and long-term memory, and retrieve information using RAG. They will set up Pinecone for vector search, use MCP to share context with external tools, and build interactive chat interfaces using Streamlit. They will also test how multiple agents can communicate and work together to complete tasks.
Each use case will reinforce key skills in reasoning, memory design, retrieval, and tool integration. In the final project, learners will build a complete AI personal assistant that uses reasoning, memory, RAG, and multi-agent features to perform real tasks such as answering questions, retrieving information, organizing steps, and interacting with external systems.
