2025 Bootcamp: Understand and Build Professional AI Agents from Udemy

The creators of the #1 Generative AI Bootcamp Worldwide (2025 Bootcamp: Generative

What are the Top Experts saying about the Potential of AI Agents:

“AI Agents are going to bring about the biggest revolution in computing." — Bill Gates, Founder of Microsoft.
“AI agents will become our digital assistants. They will make our lives easier and more efficient." — Jeff Bezos, Founder of Amazon.
“The age of AI Agents is here." — Jensen Huang, Founder of Nvidia.

Why Join This Bootcamp:

“Postings for Gen AI jobs are growing 3.5x faster than all jobs." (PwC 2024 Global Barometer)
“Jobs requiring Gen AI skills carry up to a 25% wage premium." (PwC 2024 Global Barometer)
Trusted by over 25,000 students from 140 countries — our previous Generative AI Bootcamp was ranked #1 worldwide.

What Makes This Bootcamp Special:

No prior knowledge of AI Agents is required.
Ideal next step after our 2025 Bootcamp: Generative

In Part 1, you will learn the keys to AI Agents, as well as its potential to revolutionize businesses, startups, and employment:

How AI Agents fit into the Generative AI Revolution.
What are AI Agents and Multi-Agents.
The huge market for AI Agents.
The Key Benefits of AI Agents.
Use Cases of AI Agents.
How to design a Plan to Introduce AI Agents in your company.
What are the top challenges and limitations of AI Agents.
Regulations and AI Agents: What you need to know.
Future of AI Agents.
Real Cases of AI Agents that will inspire you.

In Part 2, you will learn to build professional-level AI Agents, the most potential applications of Generative AI:

Why LangGraph is the top framework to build professional-level AI Agents today.
Why the LangChain team decided to create the LangGraph framework to build better AI Agents.
Degrees of Agentic Behavior.
What is a Graph in LangGraph?
How to learn LangGraph the right way: from painful to joyful.
Understanding the components of a LangGraph app.
AI Agents Routing with Conditional Edges.
AI Agents that remember your conversation: short-term memory.
What is in the mind of the AI Agent? The state schema.
How to change what is in the AI Agent's mind: Reducers.
Private and Public conversations: how to build AI Agents with Multiple State Schemas.
Memory efficiency: how to prevent high token usage in AI Agents.
Memory Persistence: How to save the memory of your AI Agent in an external database.
How to improve your AI Agent with Human-in-the-loop.
Breakpoints: the right time to add Human-in-the-loop.
Human-in-the-loop: how to add an approval step.
Human-in-the-loop: how to change what is in the AI Agent's mind.
Human-in-the-loop: how to debug AI Agents.
Parallelization: How your AI Agent can execute more than one task at a time.
How you can build Multi-Agents with sub-graphs.
Map-Reduce operations: how to master one of the key techniques for AI Agents.
The process to build an AI Agent from scratch: from the initial interview with your client to the final app.
How to build an advanced Multi-Agent app: automating the job of a Market Research Team.
Short-term vs. Long-term memory in AI Agents.
How to build AI Agents with long-term memory: they remember who you are, your previous conversations, and how you want them to behave.
How to manage AI Agents with complex Memory Schemas using TrustCall.
How to build an advanced AI Agent with long-term memory: an amazing personal assistant that proactively manages your to-do list for you.
How to use advanced listeners to debug your AI Agents.

Join Today: Take your place among the pioneers of the AI Agent revolution. Don’t miss this opportunity—enroll now before conditions change.

What's inside

Learning objectives

How ai agents fit into the generative ai revolution.
Real cases of ai agents that will inspire you.
How to build advanced ai agents that remember who you are and how you want them to behave.
How to build advanced multi-agents able to replace whole teams of people.
How to design a plan to introduce ai agents in your company.

The process to build an ai agent from scratch: from the initial interview with your client to the final app.
Why langgraph is the top framework to build professional-level ai agents today.
How to learn langgraph the right way.
How to improve your ai agents with human-in-the-loop and other advanced techniques.

How ai agents fit into the generative ai revolution.
Real cases of ai agents that will inspire you.
How to build advanced ai agents that remember who you are and how you want them to behave.
How to build advanced multi-agents able to replace whole teams of people.
How to design a plan to introduce ai agents in your company.
The process to build an ai agent from scratch: from the initial interview with your client to the final app.
Why langgraph is the top framework to build professional-level ai agents today.
How to learn langgraph the right way.
How to improve your ai agents with human-in-the-loop and other advanced techniques.

Syllabus

Program Presentation

See what our students say about our bootcamps

Is it recommended to enroll also in our Generative AI Bootcamp?

Alternative Learning Paths and Rhythms: Advice to find your best way to learn

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on LangGraph, a framework created by the LangChain team, suggesting a deep dive into advanced agentic workflows and complex AI architectures

Explores the potential of AI Agents to revolutionize businesses and startups, offering insights into designing plans for their implementation within a company

Requires installing notebooks and code, indicating a hands-on approach that may necessitate familiarity with software development environments and version control systems

Covers memory management techniques, such as memory summarization and persistence, which are crucial for building robust and scalable AI Agent applications

Examines the use of human-in-the-loop operations for improving and debugging AI Agents, highlighting the importance of combining AI capabilities with human oversight

Teaches LangGraph, which is still relatively new, so learners should expect to encounter rapid changes and a need for continuous learning in this evolving field

Reviews summary

Practical bootcamp on building ai agents

According to learners, this bootcamp offers a practical and hands-on approach to building professional AI agents, particularly focusing on the LangGraph framework. Students appreciate the deep dive into topics like memory persistence, human-in-the-loop workflows, and building multi-agent systems. While the course is positioned for various levels, some note that a basic understanding of Python and AI concepts is beneficial to fully grasp the material. Reviewers frequently mention the high quality of the video lectures and detailed code notebooks, which are essential for following along and applying the concepts. The course is seen as highly relevant for career advancement in the rapidly evolving AI field, providing immediately applicable skills.

High-quality videos and supporting code notebooks.

"The video lectures are clear and concise, breaking down complex topics effectively."

"Github repo with notebooks is well-organized and essential for practicing the code examples."

"Detailed notebooks accompanying the videos provide a great resource for later reference."

"Appreciated the consistent quality across the video and code materials provided."

Covers key agent topics like memory and human-in-loop.

"Modules on memory persistence, especially long-term memory, are invaluable for creating useful agents."

"Understanding human-in-the-loop operations and breakpoints was crucial for debugging and validation."

"The sections on parallelization and building multi-agents using subgraphs were advanced yet clearly explained."

"Concepts like state schemas and reducers were initially complex but well-covered."

Deep dive into a key framework for building agents.

"Really appreciated the focus on LangGraph, it feels like the right tool for complex agentic workflows."

"The detailed explanation of LangGraph nodes, edges, and state management was incredibly helpful."

"This bootcamp specifically tackles LangGraph, which many other courses gloss over or ignore completely. Crucial focus."

"Learning LangGraph deeply is a key takeaway; the course structure makes it manageable."

Emphasis on coding and building real-world agents.

"The hands-on coding and projects are the strongest part of the course for me, allowing me to actually build agents."

"Building the market research team multi-agent app was challenging but incredibly rewarding and practical."

"I learned how to use practical tools and strategies that I could apply immediately to my work."

"The notebooks and live demos make it easy to follow along and start building right away."

May require some foundational AI/coding background.

"While it says no prior knowledge required, having a basic grasp of Python and LLMs definitely helps."

"Beginners might find the pace quick in the coding sections; recommend reviewing basics first."

"Some concepts, like specific libraries or async operations, could be challenging without prior dev experience."

"Could use a bit more reinforcement on Python fundamentals for those newer to coding."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in 2025 Bootcamp: Understand and Build Professional AI Agents with these activities:

Review Foundational Generative AI Concepts

Show steps

Refresh your understanding of generative AI concepts to better grasp how AI Agents build upon them.

Browse courses on Generative AI

Show steps

Review notes from previous AI courses or bootcamps.
Read introductory articles on generative AI and LLMs.
Watch introductory videos explaining the basics of generative AI.

Build a Simple AI Agent with LangChain

Show steps

Practice building a basic AI agent using LangChain to solidify your understanding of agent architecture before diving into LangGraph.

Show steps

Choose a simple task for your AI agent (e.g., answering questions about a document).
Set up a LangChain environment and install necessary dependencies.
Implement the agent using LangChain's building blocks.
Test and refine your agent.

Read 'Building AI Agents with LangChain'

Show steps

Study a book on AI agents to gain a broader understanding of the field.

View Melania on Amazon

Show steps

Obtain a copy of 'Building AI Agents with LangChain'.
Read the book, focusing on chapters related to agent architecture and memory.
Take notes on key concepts and techniques.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Read 'AI Agents for Business'

Show steps

Study a book on AI agents to gain a broader understanding of the field.

View Melania on Amazon

Show steps

Obtain a copy of 'AI Agents for Business'.
Read the book, focusing on chapters related to business applications.
Take notes on key concepts and techniques.

Implement Memory Management Techniques

Show steps

Practice implementing different memory management techniques in LangGraph to optimize agent performance and prevent token usage issues.

Show steps

Implement short-term memory using LangGraph's state schema.
Implement memory summarization to reduce token usage.
Experiment with different memory persistence strategies.

Document Your AI Agent Project

Show steps

Create a blog post or documentation explaining your AI agent project, including the design, implementation, and challenges you faced.

Show steps

Choose a project to document.
Write a clear and concise description of your project.
Include code snippets and diagrams to illustrate your implementation.
Share your documentation with the community.

Contribute to LangGraph Documentation

Show steps

Contribute to the LangGraph documentation by fixing errors, adding examples, or improving explanations.

Show steps

Identify areas in the LangGraph documentation that need improvement.
Fork the LangGraph repository and make your changes.
Submit a pull request with your changes.

Career center

Learners who complete 2025 Bootcamp: Understand and Build Professional AI Agents will develop knowledge and skills that may be useful to these careers:

AI Agent Developer

An AI Agent Developer focuses on designing, developing, and implementing AI agents for various applications. This career role involves leveraging frameworks, such as LangGraph, to build agents that can automate tasks, make decisions, and interact with users. This course helps one become proficient in understanding the potential of AI agents and building professional-level applications. The course's emphasis on LangGraph, the top framework for building AI agents, helps one to create sophisticated agents with memory and human-in-the-loop capabilities. Furthermore, the course's coverage of multi-agent systems and real-world use cases would be directly applicable when working as an AI Agent Developer.

See salaries and explore the career path for AI Agent Developer

Generative AI Engineer

A Generative AI Engineer builds and deploys generative AI models, often working with AI agents that can create content, automate processes, and solve complex problems. This career role requires a strong understanding of AI frameworks and the ability to build applications that leverage generative AI. This course helps to understand how AI agents fit into the generative AI revolution. With its focus on using LangGraph to build professional-level AI agents, including those with memory and human-in-the-loop capabilities, it will help create sophisticated generative AI applications. Generative AI Engineer can also benefit from the course's coverage of real cases and best practices.

See salaries and explore the career path for Generative AI Engineer

Automation Specialist

An Automation Specialist is responsible for identifying opportunities to automate tasks and processes within an organization, often utilizing AI agents to achieve these goals. This role involves designing and implementing automation solutions that can improve efficiency and reduce manual effort. The course's emphasis on designing a plan to introduce AI agents into a company and building AI agents from scratch makes it particularly useful for an Automation Specialist. Learning about key benefits and use cases of AI agents, as well as mastering techniques for building multi-agent systems, helps the specialist to create innovative automation solutions.

See salaries and explore the career path for Automation Specialist

AI Solutions Architect

An AI Solutions Architect designs and implements AI-powered solutions for businesses, ensuring that these solutions meet the organization's specific needs and goals. This often involves understanding the capabilities of AI agents and integrating them into broader systems. This course helps aspiring architects because it covers a range of topics from the potential of AI agents to practical implementation with LangGraph. The course provides valuable insights into real-world use cases and best practices when working with AI agents. AI Solutions Architect can also effectively design and deploy AI solutions that incorporate AI agents.

See salaries and explore the career path for AI Solutions Architect

AI Consultant

An AI Consultant advises organizations on how to leverage artificial intelligence to improve their operations and achieve their strategic goals. This career role requires a deep understanding of AI technologies, including AI agents, and the ability to communicate the value of these technologies to clients. This course can provide consultants with a solid foundation in AI agents, including their potential, benefits, and use cases. The course's coverage of designing a plan to introduce AI agents into a company and understanding their limitations is invaluable for consultants who need to provide strategic advice to their clients. AI Consultant will be able to guide organizations in implementing AI agent solutions.

See salaries and explore the career path for AI Consultant

Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models. Increasingly, this involves working with AI agents that leverage these models to perform tasks autonomously. This course may be useful because it provides a practical introduction to building AI agents using LangGraph. The course's focus on memory persistence, human-in-the-loop techniques, and parallelization can help Machine Learning Engineer to build more sophisticated and effective AI agent applications. Furthermore, the coverage of real-world use cases and challenges of AI agents can help inform the design and deployment of machine learning models for AI agent applications.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist analyzes data to extract insights and inform decision-making. They may find this course helpful as AI agents become increasingly important for automating data analysis tasks and generating insights. Data scientists can use the skills learned in this course to build AI agents that can automate data collection, cleaning, and analysis. The course's coverage of LangGraph may help data scientists to build agents with memory and human-in-the-loop capabilities. Furthermore, the course's exploration of real-world cases can help Data Scientist to design AI agents that can effectively address data-related challenges.

See salaries and explore the career path for Data Scientist

Software Developer

A Software Developer designs, develops, and tests software applications. Software Developers needing to incorporate AI agents into their applications may find this course useful. The course's practical focus on building AI agents using LangGraph may help software developers to quickly learn how to implement AI agent functionality in their projects. Furthermore, the course's coverage of memory persistence, human-in-the-loop techniques, and parallelization may help Software Developer to build more sophisticated and scalable AI agent applications.

See salaries and explore the career path for Software Developer

Technical Product Manager

A Technical Product Manager oversees the development and launch of technical products. As AI agents become more prevalent, Technical Product Manager may find this course useful for understanding the capabilities and limitations of AI agents. This course provides a solid foundation in AI agents, including their potential, benefits, and use cases. The course's coverage of designing a plan to introduce AI agents into a company and understanding their challenges may help Technical Product Manager to make informed decisions about integrating AI agents into their products. Technical Product Manager would be able to lead the development of AI-powered products.

See salaries and explore the career path for Technical Product Manager

Business Analyst

A Business Analyst analyzes business processes and identifies opportunities for improvement. As AI agents become increasingly important for automating tasks and improving efficiency, Business Analyst may find this course helpful. The course may provide a better understanding of AI agents, their benefits, and use cases. The course's coverage of designing a plan to introduce AI agents into a company may help Business Analyst to identify opportunities for implementing AI agent solutions to streamline business processes. Business Analyst would be able to recommend and implement AI-powered solutions.

See salaries and explore the career path for Business Analyst

Research Scientist

Research Scientists who focus on artificial intelligence and multi-agent systems may find this course beneficial. Typically, this role requires a advanced degree. The course's coverage of LangGraph and techniques for building advanced AI agents helps Researchers to explore and experiment with new approaches to AI agent design and development. The knowledge gained from this course may contribute to advancements in AI agent technology.

See salaries and explore the career path for Research Scientist

Startup Founder

A Startup Founder is responsible for creating and scaling a new business. If that business involves AI, and specifically AI agents, then a startup founder may find this course of use. The course's focus on real-world use cases and the process of building an AI agent from scratch could be valuable for entrepreneurs looking to build AI agent-powered products or services. Startup Founder can gain practical skills and insights into the challenges and opportunities of building AI agent businesses.

See salaries and explore the career path for Startup Founder

Educator

An Educator who teaches courses related to artificial intelligence, machine learning, or software engineering may benefit from this course by incorporating the latest trends and technologies in AI agents into their curriculum. The course's comprehensive coverage of AI agents, LangGraph, and real-world applications may help Educators to enhance their teaching materials and provide students with practical skills that are in demand in the industry. Educator can stay up-to-date with the rapidly evolving field of AI and prepare students for careers in AI.

See salaries and explore the career path for Educator

Project Manager

A Project Manager responsible for overseeing AI-related projects finds benefit in this course. The course's coverage of designing a plan to introduce AI agents into a company may help Project Manager to better understand the scope, timeline, and resources required for AI agent projects. Project Manager can manage AI projects effectively and ensure that they are completed on time and within budget.

See salaries and explore the career path for Project Manager

IT Manager

An IT Manager who is responsible for managing an organization's technology infrastructure could use this course. IT Manager can assess which AI agent technologies are worth investing in for their organization. The course provides insights into the potential benefits and challenges of AI agents. IT Manager can also learn how to design a plan to introduce AI agents into their company, manage AI agent projects effectively, and ensure that these technologies are secure, reliable, and aligned with the organization's goals.

See salaries and explore the career path for IT Manager

Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in 2025 Bootcamp: Understand and Build Professional AI Agents.

2025 Bootcamp

Understand and Build Professional AI Agents

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Practical bootcamp on building ai agents

Activities

Career center

Reading list

Share

Similar courses