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Develop Intelligent AI Agents with OpenAI · LearnSpace
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Develop Intelligent AI Agents with OpenAI

Курс от Edureka
Средний≈ 8.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

Retrieval-Augmented GenerationMemory ManagementModel Context ProtocolAgentic systemsEmbeddingsOpenAIApplication Programming Interface (API)Generative AI AgentsContext ManagementGenerative AIPython ProgrammingChatGPTArtificial IntelligenceAgentic WorkflowsApplication DeploymentModel EvaluationOpenAI APIVector DatabasesDocument ManagementScalability

Программа курса

3 модулей · 67 учебных материалов

01Memory Systems and Knowledge Essentials23 материалов

Principles of AI Memory

Specialization IntroductionВидеоCourse IntroductionВидеоCourse OutlineЧтениеHow Memory Improves Agent IntelligenceВидео

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Edureka

Преподаватель курса

Develop Intelligent AI Agents with OpenAI
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 8.3 ч

3 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Испанский, Японский, Венгерский

Часть программы вашего университета
Short-Term vs Long-Term Memory ModelsВидео
Hands-On: Add Session Memory to an Agent Using AgentKit StorageВидео
Hands-On: Retrieve and Display Context HistoryВидео
Memory Architectures in Modern AI AgentsЧтение
What Do You Already Know About AI Memory, RAG, and Intelligent Agent Development?DIALOGUE
Practice Knowledge Check: Principles of AI MemoryЗадание

Implementing Persistent and Summarized Memory

Summarization as a Memory MechanismВидеоHands-On: Create a Memory Summarizer Agent with AgentKitВидеоHands-On: Store and Recall Conversation Summaries from DiskВидеоHands-On: Compare Raw and Summarized Memory PerformanceВидеоPractice Knowledge Check: Implementing Persistent and Summarized MemoryЗадание

Integrating Memory with Reasoning

From Reasoning to Knowledge : Memory in ActionВидеоHands-On: Extend a Reasoning Agent to Use Long-Term MemoryВидеоHands-On: Test Cross-Session Recall and Context ReinjectionВидеоRAG Pipeline Design Best PracticesЧтениеPractice Knowledge Check: Integrating Memory with ReasoningЗадание

Module Wrap-Up and Assessment

Summary of Foundations of Reasoning Agents and AgentKitЧтениеOptimizing Memory Systems to Support Coherent AI ReasoningDIALOGUEKnowledge Check: Foundations of Intelligent AgentsЗадание
02Knowledge Retrieval and Augmented Reasoning18 материалов

Understanding Retrieval-Augmented Generation (RAG)

What Is RAG and Why It MattersВидеоHow Embeddings Represent MeaningВидеоHands-On: Generate Embeddings for Documents Using OpenAI ModelsВидеоHands-On: Integrating Pinecone to the Responsive AgentВидеоPractice Knowledge Check: Designing Reasoning PromptsЗадание

Implementing Knowledge Retrieval in AgentKit

Integrating External Data with AgentKit RetrieversВидеоHands-On: Create a Retrieval Module for Local DocumentsВидеоHands-On: Combine Memory and RAG for Hybrid ContextВидеоHands-On: Evaluate Answer Accuracy and RelevanceВидеоPractice Knowledge Check: Implementing Knowledge Retrieval in AgentKitЗадание

Applying Model Context Protocol (MCP)

MCP Basics : How Agents Access and Share ContextВидеоHands-On: Connect AgentKit Memory and RAG via MCP Context FieldsВидеоHands-On: Retrieve External Knowledge on Demand Using MCPВидеоMCP Message Structure and Context SecurityЧтениеPractice Knowledge Check: Applying Model Context Protocol (MCP)Задание

Module Wrap-Up and Assessment

Summary of Building Core Agent IntelligenceЧтениеRobust Embedding and Retrieval Architectures for Knowledge-Aware AgentsDIALOGUEKnowledge Check: Building Core Agent IntelligenceЗадание
03Agentic Communication and Collaboration26 материалов

Overview of Agentic Communication Protocols

What Are A2A and ACP and How They Work with MCPВидеоCommunication Patterns : Request/Response and Peer-to-PeerВидеоHands-On: Create Two Agents (Planner & Researcher) Exchanging JSON MessagesВидеоProtocol Design and Reliability StandardsЧтениеPractice Knowledge Check: Overview of Agentic Communication ProtocolsЗадание

Implementing Agent-to-Agent Communication

Designing Agent Roles and ResponsibilitiesВидеоHands-On: Use AgentKit Sessions to Simulate Inter-Agent ChatВидеоHands-On: Share Results via A2A Protocol SchemaВидеоHands-On: Aggregate Responses in a Coordinator AgentВидеоWorkflow Design for Collaborative AgentsЧтениеPractice Knowledge Check: Implementing Agent-to-Agent CommunicationЗадание

Knowledge-Driven Communicative Agent

Hands-On: Integrate Memory, RAG, and A2A Messaging in AgentKitВидеоHands-On: Test Multi-Agent Knowledge Queries and Collaborative OutputsВидеоHands-On: Deploy the System with a Streamlit InterfaceВидеоReflection — How Communication Enables Collective ReasoningЧтениеPractice Knowledge Check: Knowledge-Driven Communicative AgentЗаданиеSummary of Advanced Integration and DeploymentЧтение

Designing and Developing Agents using Open AI Playground

What is GenAI Automation and Why It MattersВидеоExploring OpenAI PlaygroundВидеоHands On: Creating an agent using Open AI Agent BuilderВидеоPractice Knowledge Check: Designing and Developing Agents using Open AI PlaygroundЗадание

Course Wrap-Up and Assessment

Course SummaryВидеоPractice Project: Build a Context-Aware OpenAI Agent with RAG and MCPЧтениеDesigning and Integrating Memory-Driven AI Agents: A Scenario-Based Diagnostic and Implementation ExerciseDIALOGUEScenario-Based Knowledge Check: Developing Intelligent AI Agents with OpenAIЗаданиеEnd Course Knowledge Check: Develop Intelligent AI Agents with OpenAIЗадание