Building AI Agents with OpenAI

Edureka via Coursera Specialization

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Overview

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The Building AI Agents with OpenAI Specialization prepares you to design, develop, and deploy advanced AI agents using OpenAI models, AgentKit, memory systems, retrieval-augmented generation (RAG), and the Model Context Protocol (MCP). This program teaches you the same techniques used in modern AI-driven applications, personal assistants, automation systems, and multi-agent architectures. Across three hands-on courses, you will learn how AI agents plan, reason, use tools, store and retrieve memory, communicate with other agents, and connect with external applications. In Course 1, you’ll build foundational agent architectures, implement tool calling, configure environment variables securely, and create reasoning workflows. Course 2 focuses on intelligent memory design—including short-term memory, long-term memory, summarization, embeddings, vector search, and hybrid RAG + memory agents. You will also integrate knowledge retrieval using Pinecone and MCP context fields. In Course 3, you will deploy end-to-end AI assistant systems with Streamlit, create multi-agent communication flows (A2A, MCP), integrate APIs, implement personalization, and build complete cloud-ready agent applications. By the end of the specialization, you will be equipped to build scalable, production-grade AI agents capable of reasoning, recalling information, taking actions, and collaborating with other agents—skills essential for modern AI engineering and enterprise automation.

Syllabus

Course 1: Design AI Agents with OpenAI AgentKit
Course 2: Develop Intelligent AI Agents with OpenAI
Course 3: Deploy AI Agents with OpenAI

Courses

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7 hours 17 minutes
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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.
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7 hours 55 minutes
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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!
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7 hours 49 minutes
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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.