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Deploy AI Agents with OpenAI

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Deploy AI Agents with OpenAI

Instructor: Edureka

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
Flexible schedule
Learn at your own pace

Build your subject-matter expertise

This course is part of the Building AI Agents with OpenAI Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

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.

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.

What's included

12 videos4 readings4 assignments

12 videos•Total 54 minutes
  • Specialization Introduction•6 minutes
  • Course Introduction•3 minutes
  • What Is a Multi-Agent Personal Assistant?•4 minutes
  • Agent Roles : Planner, Executor, Knowledge, Interface•4 minutes
  • Modular Design Using AgentKit SDK•4 minutes
  • Hands-On: Register Specialized Agents (Reasoner, Knowledge, Action Agents)•5 minutes
  • Hands-On: Create a Coordinator Agent to Manage Sub-Agents•4 minutes
  • Hands-On: Connect Agents Using MCP for Shared Context•4 minutes
  • Agentic Protocols for Multi-Agent Systems (MCP, A2A, ACP)•4 minutes
  • Hands-On: Implement A2A Messaging Between Coordinator and Worker Agents•5 minutes
  • Hands-On: Handle Multi-Agent Responses via MCP Context Exchange•5 minutes
  • Hands-On: Aggregate and Prioritize Multi-Agent Outputs•5 minutes
4 readings•Total 40 minutes
  • Course Outline•10 minutes
  • Architectural Patterns for Multi-Agent Systems•10 minutes
  • Secure Communication Design•10 minutes
  • Summary of Integrating Intelligent Agent Components•10 minutes
4 assignments•Total 33 minutes
  • Knowledge Check: Integrating Intelligent Agent Components•15 minutes
  • Practice Quiz: System Design and Architecture•6 minutes
  • Practice Quiz: Building the Multi-Agent Framework•6 minutes
  • Practice Quiz: Communication and Collaboration•6 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.

What's included

12 videos3 readings4 assignments

12 videos•Total 67 minutes
  • Principles of Conversational UX for AI Systems•4 minutes
  • Hands-On: Build a Streamlit Chat Interface for the Assistant•6 minutes
  • Hands-On: Connect Multi-Agent Backend (AgentKit Sessions) to Frontend•6 minutes
  • Hands-On: Enable Real-Time Streaming Responses•5 minutes
  • Contextual and Personalized Assistant Behavior•3 minutes
  • Hands-On: Store User Profiles Using AgentKit Memory•7 minutes
  • Hands-On: Adapt Tone, Style, and Suggestions Dynamically•7 minutes
  • Hands-On: Maintain Long-Term Context with MCP Context Store•5 minutes
  • Connecting the Assistant to External APIs and Tools•6 minutes
  • Hands-On: Register External Tools in AgentKit (e.g., Calendar, Docs)•5 minutes
  • Hands-On: Automate Common Tasks via Function Calls and MCP Integration•4 minutes
  • Hands-On: Design a Task Completion Flow with A2A Coordination•6 minutes
3 readings•Total 30 minutes
  • Designing Ethical Personalization•10 minutes
  • Setting Up Google Calendar and Google Docs API from Google Cloud Console•10 minutes
  • Summary of Designing User Interaction and Personalization•10 minutes
4 assignments•Total 33 minutes
  • Knowledge Check: Designing User Interaction and Personalization•15 minutes
  • Practice Quiz: Designing Conversational Interfaces•6 minutes
  • Practice Quiz: Implementing Personalization•6 minutes
  • Practice Quiz: Automating External Actions•6 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.

What's included

10 videos4 readings5 assignments

10 videos•Total 42 minutes
  • Validating Multi-Agent Communication and Logic•4 minutes
  • Hands-On: Write Test Cases for Reasoning and Coordination Flows - I•5 minutes
  • Hands-On: Write Test Cases for Reasoning and Coordination Flows - II•5 minutes
  • Hands-On: Measure Response Accuracy and Latency•7 minutes
  • Deployment Options : Streamlit Cloud•5 minutes
  • Hands-On: Package the Assistant for Cloud Deployment•4 minutes
  • Hands-On: Set Up API Keys and Environment Variables Securely•3 minutes
  • Hands-On: Enable Multi-Agent Sessions in Cloud Environments•4 minutes
  • Hands-On: Deploy the Multi-Agent Assistant with Streamlit Interface•4 minutes
  • Course Summary•2 minutes
4 readings•Total 50 minutes
  • Debugging and Log Analysis Techniques•10 minutes
  • Reflection — Designing Responsible, Scalable Assistants•10 minutes
  • Summary of Deployment, Testing, and Optimization•10 minutes
  • Practice Project: Deploying and Scaling an OpenAI-Powered Multi-Agent Personal Assistant•20 minutes
5 assignments•Total 68 minutes
  • Develop Intelligent AI Agents with OpenAI – Scenario Based•20 minutes
  • End Course Knowledge Check: Develop AI Agents with OpenAI•30 minutes
  • Practice Quiz: Testing and Validation•6 minutes
  • Practice Quiz: Deployment and Scaling•6 minutes
  • Practice Quiz: Deploying AI Personal Assistant System•6 minutes

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Instructor

Edureka
203 Courses•185,724 learners

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Frequently asked questions

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

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

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

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.

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

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

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.

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.

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.

Financial aid available,

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