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Design AI Agents with OpenAI AgentKit · LearnSpace
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courseraПрограммирование

Design AI Agents with OpenAI AgentKit

Курс от Edureka
Средний≈ 8.9 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course explores how to design and build intelligent, reasoning-based AI agents using OpenAI tools, combining structured reasoning, function calling, memory, and communication to create dynamic, context-aware systems. Designed for developers and AI enthusiasts who want to go beyond prompt engineering, it demonstrates how modern agent frameworks like AgentKit and the Model Context Protocol (MCP) enable agents to reason, plan, and act autonomously using context, tools, and collaboration. Through guided lessons and hands-on demonstrations, you’ll learn to set up your development environment, integrate OpenAI’s APIs, and design reasoning-driven workflows that mimic human-like problem solving. You will explore how agents use planning, reflection, and self-correction, implement function calling and tool use, manage short- and long-term memory, and establish agent-to-agent communication for collaborative decision-making. The course culminates in building a fully functional reasoning agent system with a Streamlit-based UI, integrating prompts, memory, tools, and communication into one cohesive framework. By the end of this course, you will be able to: - Explain the anatomy of intelligent agents, including reasoning, memory, tools, and context. - Set up the OpenAI API, configure environment variables, and initialize AgentKit for agent development. - Design and implement structured reasoning workflows using prompts and reflection-based logic. - Integrate function calling and tool registration for agents to perform dynamic tasks autonomously. - Add short-term and contextual memory for improved continuity and understanding across sessions. - Build multi-agent communication systems using the Model Context Protocol (MCP). - Develop and deploy an interactive reasoning agent application using Streamlit. This course is ideal for software developers, data scientists, and AI practitioners who want to build autonomous, reasoning-powered applications using OpenAI’s ecosystem. A working knowledge of Python and basic familiarity with APIs or AI models will be helpful, but no prior experience with agent frameworks is required. Join us to master the next generation of AI development — and learn how to transform models into intelligent, context-aware agents that think, plan, and communicate like real collaborators!

Навыки, которые вы освоите

Context ManagementTool CallingAgentic systemsMemory ManagementGenerative AI AgentsPrompt EngineeringDevelopment EnvironmentAI IntegrationsLarge Language ModelingOpenAIChatGPTLLM ApplicationDecision IntelligenceApplication DeploymentApplication Programming Interface (API)Python ProgrammingAgentic WorkflowsOpenAI APIArtificial IntelligenceModel Context Protocol

Программа курса

3 модулей · 75 учебных материалов

01Foundations of Intelligent Agents29 материалов

Understanding Intelligent Agents

Specialization IntroductionВидеоCourse IntroductionВидеоCourse OutlineЧтениеWhat Are AI Agents and Why They MatterВидео

Учитесь у экспертов

Edureka

Преподаватель курса

Design AI Agents with OpenAI AgentKit
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 8.9 ч

3 модулей

Язык: Английский

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

Часть программы вашего университета
Agents vs. Chatbots : From Reactivity to ReasoningВидео
Anatomy of an Agent: Reasoning, Memory, Tools, and ContextЧтение
Your Intelligent Reasoning Agent Readiness CheckDIALOGUE
Practice Knowledge Check: Understanding Intelligent AgentsЗадание

Agent Architecture and Environment Setup

Designing the Structure of an AI AgentВидеоHands-On: Setting Up the OpenAI API and Configuring Environment VariablesВидеоHands-On: Initializing AgentKitВидеоHands-On: Running Your First AgentKit Chat SessionВидеоKey AgentKit Components: Sessions, Tools, and MemoryЧтениеResponsible AI Usage and API Key ManagementЧтениеPractice Knowledge Check: Agent Architecture and Environment SetupЗадание

Inside the Agent’s Reasoning Process

How Agents Think : Planning and Self-ReflectionВидеоChain-of-Thought Reasoning and Self-CorrectionВидеоHands-On: Writing a “Think-Then-Answer” Prompt Using the OpenAI APIВидеоHands-On: Implementing the Same Logic Using AgentKit’s Reasoning WorkflowВидеоBest Practices for Controlling Model BehaviorЧтениеPractice Knowledge Check: Inside the Agent’s Reasoning ProcessЗадание

Introducing Agentic Communication Concepts

How Agents Share Context : The Role of MCPВидеоBasics of Agentic Communication (A2A & Message Passing)ВидеоHands-On: Simulate Message Exchange Between Two Agents Using JSON MessagesВидеоOverview of Agent Communication Protocol StandardsЧтениеPractice Knowledge Check: Introducing Agentic Communication ConceptsЗадание

Module Wrap-Up and Assessment

Summary of Foundations of Reasoning Agents and AgentKitЧтениеEstablishing Intelligent Agent Systems Through Planning, Self-Reflection, and Context ExchangeDIALOGUEKnowledge Check: Foundations of Intelligent AgentsЗадание
02Building Core Agent Intelligence23 материалов

Designing Reasoning Prompts

What Is Structured Reasoning and Why It WorksВидеоHands-On: Build a Step-by-Step Reasoning Prompt TemplateВидеоHands-On: Implement a “Reflect and Revise” Prompt for Error CorrectionВидеоPractice Knowledge Check Designing Reasoning PromptsЗадание

Function Calling and Tool Use

Understanding Function Calling in OpenAI ModelsВидеоHands-On: Define Custom FunctionsВидеоHands On-Register and Auto-Select Tools with AgentKitВидеоDesigning Robust Function SchemasЧтениеPractice Knowledge Check: Function Calling and Tool UseЗадание

Adding Short-Term Memory

What Is Context Memory in AI Agents?ВидеоHands-On: Use AgentKit’s SessionMemory to Store Recent InteractionsВидеоHands-On: Summarize and Recall Prior Conversations DynamicallyВидеоHands-On: Reset or Continue Conversations ContextuallyВидеоDesigning Stateful vs. Stateless Agent BehaviorЧтениеPractice Knowledge Check: Adding Short-Term MemoryЗадание

Building the Reasoning Core

Connecting Prompts, Tools, and MemoryВидеоHands-On: Create a Complete Reasoning Agent PipelineВидеоHands-On: Add Input Validation and LoggingВидеоHands-On: Test the Agent with Complex Multi-Step PromptsВидеоPractice Knowledge Check: Building the Reasoning CoreЗадание

Module Wrap-Up and Assessment

Summary of Building Core Agent IntelligenceЧтениеCrafting Deep Reasoning Pipelines With Prompts, Function Calling, and Contextual Memory RetrievalDIALOGUEKnowledge Check: Building Core Agent IntelligenceЗадание
03Advanced Integration and Deployment23 материалов

Planning and Architecture

Designing the Intelligent Reasoning Agent SystemВидеоHands-On: Map Out the Agent’s Data Flow (Prompt → Tool → Memory → Output)ВидеоHands-On: Integrate MCP for Structured Context ManagementВидеоUse Cases: Problem Solving, Tutoring, and SummarizationЧтениеPractice Knowledge Check: Planning and ArchitectureЗадание

Implementation and Testing

Hands On: Building the Full Agent in VS CodeВидеоHands-On: Code Integration : AgentKit Functions + Memory + MCP ContextВидеоHands-On: Test Multiple Real-World Scenarios (Math, Summaries, Planning)ВидеоHands-On: Debug Common Agent Issues and Improve AccuracyВидеоOptimizing Reasoning Agents for PerformanceЧтениеPractice Knowledge Check: Implementation and TestingЗадание

Interface and Deployment

Creating a User Interface with StreamlitВидеоHands-On: Build a Chat UI to Interact with the Reasoning Agent - IВидеоHands-On: Build a Chat UI to Interact with the Reasoning Agent - IIВидеоHands-On: Connect the Frontend to Your AgentKit BackendВидеоHands-On: Deploy the Agent on Streamlit CloudВидеоPractice Knowledge Check: Interface and DeploymentЗадание

Course Wrap-Up and Assessment

Course SummaryВидеоPractice Project: Build an Intelligent Reasoning AgentЧтениеThe Final Check-In: Designing and Deploying Intelligent Reasoning AgentsDIALOGUEDesigning a Reasoning Agent with AgentKit – Scenario AssignmentЗаданиеEnd Course Knowledge Check: Building Reasoning Agents with OpenAIЗадание
Summary of Advanced Integration and DeploymentЧтение