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Deploy AI Agents with OpenAI · LearnSpace
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courseraПрограммирование

Deploy AI Agents with OpenAI

Курс от Edureka
Средний≈ 7.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course teaches you how to deploy fully functional, multi-agent AI systems using OpenAI’s latest tools and frameworks. You will learn how intelligent agents communicate, coordinate, and execute tasks together—then bring those capabilities into real-world applications through interactive interfaces and cloud deployment workflows. Through hands-on lessons and guided demos, you’ll design and implement multi-agent architectures, build conversational interfaces with Streamlit, integrate external APIs, and enable structured communication using the Model Context Protocol (MCP) and Agent-to-Agent (A2A) messaging. You will also learn to secure your deployments, manage environment variables, monitor system performance, and ensure scalable, reliable operation across users and workloads. By the end of this course, you will be able to: - Explain the structure and roles of multi-agent systems, including coordinator, planner, reasoning, retrieval, and action agents. - Design and implement multi-agent communication workflows using MCP contexts and A2A message passing. - Build and deploy an interactive user interface using Streamlit to enable real-time agent interaction. - Connect the agent backend to external tools and APIs, enabling real-world task execution and workflow automation. - Deploy your multi-agent assistant securely to the cloud, managing API keys, environment variables, and runtime configurations. - Monitor, optimize, and scale multi-agent performance using practical evaluation metrics and deployment best practices. This course is ideal for AI engineers, software developers, automation professionals, and technical leaders who want to build production-ready AI assistants, agentic applications, and enterprise-grade multi-agent systems. A basic understanding of Python, APIs, and foundational AI agent concepts is recommended. Join us to learn how to deploy intelligent multi-agent systems that are scalable, reliable, and ready for real-world use.

Навыки, которые вы освоите

Agentic systemsAI PersonalizationAgentic WorkflowsAI OrchestrationApplication DeploymentAI IntegrationsResponsible AICloud APIGenerative AI AgentsOpenAICloud DeploymentOpenAI APIGenerative AIArtificial IntelligenceModel Context ProtocolModel DeploymentCI/CDChatGPTDevelopment TestingAI Workflows

Программа курса

3 модулей · 62 учебных материалов

01Integrating Intelligent Agent Components22 материалов

System Design and Architecture

Specialization IntroductionВидеоCourse IntroductionВидеоCourse OutlineЧтениеWhat Is a Multi-Agent Personal Assistant?Видео

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Edureka

Преподаватель курса

Deploy AI Agents with OpenAI
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 7.8 ч

3 модулей

Язык: Английский

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

Часть программы вашего университета
Agent Roles : Planner, Executor, Knowledge, InterfaceВидео
Architectural Patterns for Multi-Agent SystemsЧтение
What Do You Already Know About Deploying Multi-Agent AI Systems?DIALOGUE
Practice Quiz: System Design and ArchitectureЗадание

Building the Multi-Agent Framework

Modular Design Using AgentKit SDKВидеоHands-On: Register Specialized Agents (Reasoner, Knowledge, Action Agents)ВидеоHands-On: Create a Coordinator Agent to Manage Sub-AgentsВидеоHands-On: Connect Agents Using MCP for Shared ContextВидеоPractice Quiz: Building the Multi-Agent FrameworkЗадание

Communication and Collaboration

Agentic Protocols for Multi-Agent Systems (MCP, A2A, ACP)ВидеоHands-On: Implement A2A Messaging Between Coordinator and Worker AgentsВидеоHands-On: Handle Multi-Agent Responses via MCP Context ExchangeВидеоHands-On: Aggregate and Prioritize Multi-Agent OutputsВидеоSecure Communication DesignЧтениеPractice Quiz: Communication and CollaborationЗадание

Module Wrap-Up and Assessment

Summary of Integrating Intelligent Agent ComponentsЧтениеArchitecting Distributed Agents for Reliable Coordination and Task ExecutionDIALOGUEKnowledge Check: Integrating Intelligent Agent ComponentsЗадание
02Designing User Interaction and Personalization20 материалов

Designing Conversational Interfaces

Principles of Conversational UX for AI SystemsВидеоHands-On: Build a Streamlit Chat Interface for the AssistantВидеоHands-On: Connect Multi-Agent Backend (AgentKit Sessions) to FrontendВидеоHands-On: Enable Real-Time Streaming ResponsesВидеоPractice Quiz: Designing Conversational InterfacesЗадание

Implementing Personalization

Contextual and Personalized Assistant BehaviorВидеоHands-On: Store User Profiles Using AgentKit MemoryВидеоHands-On: Adapt Tone, Style, and Suggestions DynamicallyВидеоHands-On: Maintain Long-Term Context with MCP Context StoreВидеоDesigning Ethical PersonalizationЧтениеPractice Quiz: Implementing PersonalizationЗадание

Automating External Actions

Connecting the Assistant to External APIs and ToolsВидеоHands-On: Register External Tools in AgentKit (e.g., Calendar, Docs)ВидеоHands-On: Automate Common Tasks via Function Calls and MCP IntegrationВидеоHands-On: Design a Task Completion Flow with A2A CoordinationВидеоSetting Up Google Calendar and Google Docs API from Google Cloud ConsoleЧтениеPractice Quiz: Automating External ActionsЗадание

Module Wrap-Up and Assessment

Summary of Designing User Interaction and PersonalizationЧтениеDelivering Personalized Conversational Experiences with Integrated Automation and Context ManagementDIALOGUEKnowledge Check: Designing User Interaction and PersonalizationЗадание
03Deployment, Testing, and Optimization20 материалов

Testing and Validation

Validating Multi-Agent Communication and LogicВидеоHands-On: Write Test Cases for Reasoning and Coordination Flows - IВидеоHands-On: Write Test Cases for Reasoning and Coordination Flows - IIВидеоHands-On: Measure Response Accuracy and LatencyВидеоDebugging and Log Analysis TechniquesЧтениеPractice Quiz: Testing and ValidationЗадание

Deployment and Scaling

Deployment Options : Streamlit CloudВидеоHands-On: Package the Assistant for Cloud DeploymentВидеоHands-On: Set Up API Keys and Environment Variables SecurelyВидеоHands-On: Enable Multi-Agent Sessions in Cloud EnvironmentsВидеоPractice Quiz: Deployment and ScalingЗадание

Deploying AI Personal Assistant System

Hands-On: Deploy the Multi-Agent Assistant with Streamlit InterfaceВидеоReflection — Designing Responsible, Scalable AssistantsЧтениеPractice Quiz: Deploying AI Personal Assistant SystemЗаданиеSummary of Deployment, Testing, and OptimizationЧтение

Course Wrap-Up and Assessment

Course SummaryВидеоDeploying Multi-Agent AI Assistant Systems: A Scenario-Based Diagnostic and Implementation ExerciseDIALOGUEPractice Project: Deploying and Scaling an OpenAI-Powered Multi-Agent Personal AssistantЧтениеDevelop Intelligent AI Agents with OpenAI – Scenario BasedЗаданиеEnd Course Knowledge Check: Develop AI Agents with OpenAIЗадание