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Implementation of GenAI Agents

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Implementation of GenAI Agents

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Gain insight into a topic and learn the fundamentals.
4.5

134 reviews

Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
4.5

134 reviews

Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Apply core principles of AI agent architecture to design a basic agent system

  • Construct a development environment for building and testing AI agents

  • Develop a functional AI agent using a chosen framework (e.g., LangChain or AutoGen)

  • Evaluate and optimize an AI agent's performance through advanced feature integration

Details to know

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Assessments

2 assignmentsΒΉ

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Building GenAI Applications and Agents 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 is 1 module in this course

This course offers a fast-paced, hands-on introduction to the world of AI agents, perfect for aspiring AI architects and innovators. In just 75 minutes, you'll develop the skills to build AI agents that can understand, reason, and act in real-world scenarios. With a focus on efficiency and practical development, you'll dive straight into coding while gaining techniques that are scalable for future projects.

This course is crafted for software developers, AI engineers, data scientists, and data and business analysts who are keen to implement AI in real-world scenarios. If you're interested in expanding your technical skills and gaining hands-on experience with AI agent development, this is the perfect starting point. Whether you're aiming to enhance existing applications, explore AI-powered solutions, or bring new ideas to life, this course equips you with essential skills for AI-driven innovation. This course is designed to be accessible to learners with a foundational understanding of Python programming and a general awareness of AI concepts; advanced AI expertise is not required. To participate fully, you’ll need a computer with a reliable internet connection, as the course involves hands-on coding exercises and interactive problem-solving. An openness to practical, step-by-step learning and real-world application is key, as this course emphasizes a mix of theory and immediate implementation. By the end of this course, learners will have the skills to apply core principles of AI agent architecture, enabling them to design and implement a basic agent system. You'll gain the capability to construct an efficient development environment for building and testing your AI agents, facilitating smooth workflows and testing processes. Additionally, you’ll develop a fully functional AI agent using frameworks like LangChain or AutoGen and learn to evaluate and optimize its performance through advanced feature integration, enhancing your agent’s effectiveness and adaptability in real-world applications.

This course offers a fast-paced, hands-on introduction to the world of AI agents, perfect for aspiring AI architects and innovators. Emphasizing practical application and fast-track learning, you’ll immerse yourself in coding while acquiring scalable skills for future projects.

What's included

15 videos7 readings2 assignments3 discussion prompts

15 videosβ€’Total 88 minutes
  • Introduction to the Course & Meet Your Instructorβ€’2 minutes
  • Fundamentals of AI Agent Architecture β€’7 minutes
  • Architecting Your AI Agent's Blueprint β€’7 minutes
  • Designing Your Agent's Decision-Making Core β€’6 minutes
  • Mapping Your Agent's Interaction Flow β€’6 minutes
  • Structuring Your Agent's Knowledge Base β€’5 minutes
  • Constructing Your AI Development Lab β€’5 minutes
  • Integrating AI Frameworks and Libraries β€’3 minutes
  • Implementing Your Agent with LangChain/AutoGen β€’5 minutes
  • Coding Core Agent Functionalities β€’8 minutes
  • Evaluating Your AI Agent's Performance β€’7 minutes
  • Enhancing Your Agent with Memory Capabilities β€’7 minutes
  • Optimizing Your Agent with Advanced Tools β€’6 minutes
  • Fine-Tuning Your AI Assistant for Peak Performance β€’12 minutes
  • Congratulations and Continuous Learning Journeyβ€’2 minutes
7 readingsβ€’Total 50 minutes
  • Welcome to the Course: Course Overview β€’5 minutes
  • Hands On Learning (HOL): Designing Your Own AI Agent Architectureβ€’10 minutes
  • AI Agent Architectures: From Theory to Practice β€’5 minutes
  • Hands On Learning (HOL): Implementing Core AI Agent Functionalitiesβ€’10 minutes
  • Best Practices in AI Development Environments and Framework Selection β€’5 minutes
  • Cutting-Edge Techniques in AI Agent Optimizationβ€’5 minutes
  • Hands On Learning (HOL): Optimizing and Evaluating Your AI Research Assistantβ€’10 minutes
2 assignmentsβ€’Total 50 minutes
  • Implementation of GenAI Agentsβ€’20 minutes
  • GenAI-Empowered Personal Assistant Development Reportβ€’30 minutes
3 discussion promptsβ€’Total 15 minutes
  • Enhancing AI Assistants with an Innovative Componentβ€’5 minutes
  • Impact of Knowledge Base Technology on AI Agent Performanceβ€’5 minutes
  • (Optional) Optimizing Research through Multi-Source and Parallel Processingβ€’5 minutes

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Instructors

Instructor ratings
4.4 (61 ratings)
Coursera
27 Coursesβ€’25,234 learners

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JN
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Reviewed on Jan 26, 2026

Simple and good briefing on the key steps of implementation of GenAI Agents.

Frequently asked questions

In this course, a GenAI agent is a connected AI system that gathers information, reasons over it, generates responses, uses stored context, and improves through feedback. The emphasis is on designing and implementing a basic agent architecture that you can build, test, and refine in code.

You would use a GenAI agent when a task involves linked steps such as collecting information, analyzing it, generating an output, and improving based on later input. In this course, the focus is on situations where a structured system is more useful than an isolated AI interaction.

A GenAI agent connects earlier design decisions with later testing and improvement, turning an AI idea into a system you can actually run and refine. The course treats agent development as a repeatable workflow that includes architecture, implementation, retrieval, response generation, and evaluation.

A GenAI agent is built as a multi-part system, while a one-off AI response is just a single output from a prompt. The course focuses on agents that combine reasoning, memory, retrieval, and feedback so the work can continue across tasks instead of ending after one answer.

A basic understanding of Python and general AI concepts is helpful before learning to build a GenAI agent. You do not need advanced AI expertise, but you should be comfortable following hands-on coding and practical problem-solving.

The course uses Python with an agent-building framework such as LangChain or AutoGen. It also covers information gathering and knowledge-base retrieval as core parts of the agent workflow.

You practice designing an agent architecture, setting up the coding environment, implementing information gathering and response generation, and organizing a basic knowledge base for retrieval. You also evaluate the agent's outputs and refine the system through memory, feedback, and broader information gathering.

Financial aid available,

ΒΉ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.