What is the focus of the final phase of the “Develop Intelligent AI Agents with OpenAI” course?

The final phase focuses on testing, validating, and deploying multi-agent systems to ensure reliability, accuracy, scalability, and production readiness.

Will I learn how to test multi-agent reasoning and coordination flows?

Yes. You will write structured test cases to validate reasoning steps, agent communication, MCP message exchanges, and overall workflow correctness.

Does the course cover performance evaluation for AI agents?

Absolutely. You will measure response accuracy, latency, retrieval quality, and grounding strength to ensure agents behave consistently in real scenarios.

Are CI/CD practices included?

Yes. You’ll understand how to integrate automated testing, model updates, and version control into a simple and effective CI/CD workflow for AI projects.

Do I get hands-on experience with scaling multi-agent systems?

Yes. The course covers scalability considerations such as concurrency handling, cost management, API quotas, and optimizing multi-agent execution.

What will I be able to demonstrate after completing the final section of the course?

You will be able to design, test, debug, deploy, and scale a production-ready multi-agent AI assistant with secure configuration and reliable performance.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Deploy AI Agents with OpenAI

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Deploy AI Agents with OpenAI

Ce cours fait partie de Spécialisation "Building AI Agents with OpenAI"

Instructeur : Edureka

Inclus avec

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

7 heures à compléter

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

7 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Compétences que vous acquerrez

Catégorie : Generative AI Agents
Catégorie : Agentic systems
Catégorie : Artificial Intelligence
Catégorie : Application Deployment
Catégorie : Development Testing
Catégorie : AI Integrations
Catégorie : Cloud API
Catégorie : Responsible AI
Catégorie : CI/CD
Catégorie : AI Personalization
Catégorie : Cloud Deployment

Outils que vous découvrirez

Catégorie : OpenAI
Catégorie : OpenAI API
Catégorie : ChatGPT
Catégorie : AI Orchestration
Catégorie : AI Workflows
Catégorie : Model Deployment
Catégorie : Agentic Workflows
Catégorie : Generative AI
Catégorie : Model Context Protocol

Détails à connaître

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Ce cours fait partie de la Spécialisation "Building AI Agents with OpenAI"

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Il y a 3 modules dans ce cours

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.

Détails du module

This module introduces the architecture and design principles behind building multi-agent personal assistant systems. Learners will explore the roles of planner, executor, knowledge, and interface agents and understand how these components collaborate through the Model Context Protocol (MCP). Through guided hands-on exercises with the AgentKit SDK, you’ll design modular frameworks, connect agents for shared context, and implement secure communication patterns that enable intelligent coordination and reliability across agent workflows.

Inclus

12 vidéos4 lectures4 devoirs

12 vidéosTotal 54 minutes

Specialization Introduction6 minutes
Course Introduction3 minutes
What Is a Multi-Agent Personal Assistant?4 minutes
Agent Roles : Planner, Executor, Knowledge, Interface4 minutes
Modular Design Using AgentKit SDK4 minutes
Hands-On: Register Specialized Agents (Reasoner, Knowledge, Action Agents)5 minutes
Hands-On: Create a Coordinator Agent to Manage Sub-Agents4 minutes
Hands-On: Connect Agents Using MCP for Shared Context4 minutes
Agentic Protocols for Multi-Agent Systems (MCP, A2A, ACP)4 minutes
Hands-On: Implement A2A Messaging Between Coordinator and Worker Agents5 minutes
Hands-On: Handle Multi-Agent Responses via MCP Context Exchange5 minutes
Hands-On: Aggregate and Prioritize Multi-Agent Outputs5 minutes

4 lecturesTotal 40 minutes

Course Outline10 minutes
Architectural Patterns for Multi-Agent Systems10 minutes
Secure Communication Design10 minutes
Summary of Integrating Intelligent Agent Components10 minutes

4 devoirsTotal 33 minutes

Knowledge Check: Integrating Intelligent Agent Components15 minutes
Practice Quiz: System Design and Architecture6 minutes
Practice Quiz: Building the Multi-Agent Framework6 minutes
Practice Quiz: Communication and Collaboration6 minutes

This module focuses on building user-facing, intelligent personal assistants that deliver seamless conversational experiences. You’ll learn to design intuitive chat interfaces using Streamlit, connect multi-agent backends via AgentKit sessions, and enable real-time streaming responses. The module also explores personalization strategies—storing user profiles, adapting behavior dynamically, and maintaining long-term context with MCP. Finally, you’ll implement automation by integrating external APIs and tools, enabling your assistant to execute real-world actions responsibly and efficiently.

Inclus

12 vidéos3 lectures4 devoirs

12 vidéosTotal 67 minutes

Principles of Conversational UX for AI Systems4 minutes
Hands-On: Build a Streamlit Chat Interface for the Assistant6 minutes
Hands-On: Connect Multi-Agent Backend (AgentKit Sessions) to Frontend6 minutes
Hands-On: Enable Real-Time Streaming Responses5 minutes
Contextual and Personalized Assistant Behavior3 minutes
Hands-On: Store User Profiles Using AgentKit Memory7 minutes
Hands-On: Adapt Tone, Style, and Suggestions Dynamically7 minutes
Hands-On: Maintain Long-Term Context with MCP Context Store5 minutes
Connecting the Assistant to External APIs and Tools6 minutes
Hands-On: Register External Tools in AgentKit (e.g., Calendar, Docs)5 minutes
Hands-On: Automate Common Tasks via Function Calls and MCP Integration4 minutes
Hands-On: Design a Task Completion Flow with A2A Coordination6 minutes

3 lecturesTotal 30 minutes

Designing Ethical Personalization10 minutes
Setting Up Google Calendar and Google Docs API from Google Cloud Console10 minutes
Summary of Designing User Interaction and Personalization10 minutes

4 devoirsTotal 33 minutes

Knowledge Check: Designing User Interaction and Personalization15 minutes
Practice Quiz: Designing Conversational Interfaces6 minutes
Practice Quiz: Implementing Personalization6 minutes
Practice Quiz: Automating External Actions6 minutes

This module guides learners through validating, deploying, and scaling intelligent multi-agent personal assistant systems. You’ll begin by testing reasoning and coordination flows, writing structured test cases, and analyzing performance through response accuracy and latency metrics. Then, you’ll package and deploy your assistant using Streamlit Cloud, manage environment configurations, and enable secure, multi-agent sessions at scale. The module concludes with a capstone project where you’ll deploy a fully functional AI personal assistant, applying best practices for testing, documentation, and responsible AI deployment.

Inclus

10 vidéos4 lectures5 devoirs

10 vidéosTotal 42 minutes

Validating Multi-Agent Communication and Logic4 minutes
Hands-On: Write Test Cases for Reasoning and Coordination Flows - I5 minutes
Hands-On: Write Test Cases for Reasoning and Coordination Flows - II5 minutes
Hands-On: Measure Response Accuracy and Latency7 minutes
Deployment Options : Streamlit Cloud5 minutes
Hands-On: Package the Assistant for Cloud Deployment4 minutes
Hands-On: Set Up API Keys and Environment Variables Securely3 minutes
Hands-On: Enable Multi-Agent Sessions in Cloud Environments4 minutes
Hands-On: Deploy the Multi-Agent Assistant with Streamlit Interface4 minutes
Course Summary2 minutes

4 lecturesTotal 50 minutes

Debugging and Log Analysis Techniques10 minutes
Reflection — Designing Responsible, Scalable Assistants10 minutes
Summary of Deployment, Testing, and Optimization10 minutes
Practice Project: Deploying and Scaling an OpenAI-Powered Multi-Agent Personal Assistant20 minutes

5 devoirsTotal 68 minutes

Develop Intelligent AI Agents with OpenAI – Scenario Based20 minutes
End Course Knowledge Check: Develop AI Agents with OpenAI30 minutes
Practice Quiz: Testing and Validation6 minutes
Practice Quiz: Deployment and Scaling6 minutes
Practice Quiz: Deploying AI Personal Assistant System6 minutes

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