Overview

This course teaches you how to build AI agents that can remember, retrieve, and reason using OpenAI’s advanced memory and retrieval capabilities. You will learn how modern intelligent systems store context, embed knowledge, summarize conversations, and access relevant information through Retrieval-Augmented Generation (RAG). These skills form the core of powerful enterprise-grade AI agents capable of long-term coherence, personalized responses, and deep contextual understanding. Through hands-on lessons and guided demos, you’ll explore how to design short-term and long-term memory pipelines, implement embedding-based vector search, integrate document retrieval, and connect multi-agent workflows using the Model Context Protocol (MCP). You will learn how to combine memory, knowledge retrieval, and reasoning to build agents that are scalable, accurate, and aligned with real-world use cases. By the end of this course, you will be able to: - Explain how memory systems, embeddings, and RAG enhance agent intelligence and long-term contextual reasoning. - Implement short-term and long-term memory pipelines, including session memories, summarization, and vector storage. - Generate and use embeddings to power semantic search, document retrieval, and hybrid knowledge workflows. - Build agents that combine retrieval and reasoning, integrating RAG into core decision-making - Use MCP context fields to connect multiple agents, enabling shared memory and collaborative task execution. - Evaluate memory quality, retrieval relevance, and hallucination risks using best-practice metrics. This course is ideal for AI developers, data engineers, software professionals, and technical decision-makers who want to build context-aware, retrieval-driven, and memory-enabled AI agents for production use. A basic understanding of Python, APIs, and foundational AI prompting concepts is recommended. Join us to master the essential building blocks of intelligent agents—and create systems that truly understand, recall, and reason.

Syllabus

Memory Systems and Knowledge Essentials

This module establishes the foundational understanding of how memory enhances the intelligence and adaptability of AI agents. Learners will explore short-term, long-term, and summarized memory architectures and implement them using AgentKit. Through practical exercises, you will design agents capable of storing, recalling, and summarizing contextual information to enable continuity and reasoning across sessions.

Knowledge Retrieval and Augmented Reasoning

This module focuses on empowering AI agents with retrieval-augmented generation (RAG) and interoperable context sharing through the Model Context Protocol (MCP). Learners will gain hands-on experience in generating embeddings, managing vector databases, and building hybrid systems that combine memory and retrieval. The module culminates in connecting RAG pipelines with MCP for dynamic, knowledge-driven agent intelligence.

Agentic Communication and Collaboration

This module delves into the design and implementation of multi-agent communication systems. Learners will explore Agent-to-Agent (A2A) and Agentic Communication Protocols (ACP) built on MCP to enable structured collaboration among agents. Through guided projects, you will develop specialized agents that exchange data, coordinate reasoning, and deploy integrated, knowledge-driven systems for collective problem-solving.