How to build AI agents with Microsoft AutoGen for agentic AI

TL;DR

Autogen enables building autonomous, multi agent AI systems and the course teaches setting up, configuring, and deploying agents and chat bots. It covers Python prerequisites, single and multi agent workflows, human in the loop, performance metrics, and real world deployment, with a 32.5 hour curriculum and lifetime access.

Transcript

Hello all, my name is Krishna and welcome to my YouTube channel. So guys, I am super excited to announce our new Udemy course that is nothing but building AI agents and agentic AI systems with the help of Microsoft Autogen. Now if you don't know Microsoft Autogen is also one of the most popular framework that will be specifically used to develop ag... Read More

Key Insights

• AutoGen is the primary framework for building agentic AI applications in this course.
• The curriculum covers from setup to deployment of AI agents and agentic systems.
• The course emphasizes multi agent coordination and agent collaboration across workflows.
• Python programming and basic LM model knowledge are listed prerequisites.
• The instructors promise lifetime access and a 30 day refund policy.
• Project driven learning is paired with real world use cases and performance metrics.
• The content includes instruction on Autogen Studio and deployment considerations.
• There is a focus on autonomous reasoning, planning, and action by agents.
• More videos from this channel:
  • How to use AI for coding with wipe coding
  • How to build agents with OpenAI Agent Toolkit
  • How to build a knowledge graph with LangChain and Neo4j
• More from this channel: Krish Naik

Questions & Answers

Q: What is the main skill taught in this course about AI agents?

The main skill is building autonomous AI agents and agentic AI systems using Microsoft AutoGen, including how agents interact, cooperate, and deploy in real world tasks. Learners will gain practical experience in configuring the framework, designing custom agents, and deploying end to end workflows with multi agent collaboration.

Q: What prerequisites are required for learners?

Learners should have some Python programming knowledge and understand modular coding. A basic understanding of LM models and generative AI concepts is also recommended to effectively follow the course material and implement agents with AutoGen.

Q: Does the course cover both single agent and multi agent workflows?

Yes, the course covers creating a single agent as well as multi agent communication and collaboration. It emphasizes building powerful agents that can automate tasks and coordinate with others to complete complex end to end processes.

Q: What kind of projects or real world tasks does the course include?

The course includes projects and use cases that demonstrate building and deploying real world multi agent workflows, automating complex tasks, and enabling agents to interact with external tools as part of end to end solutions.

Q: Is there any discount or coupon mentioned for enrollment?

Yes, the description mentions a coupon code AGENTIC01 offering a reduced price, with the total price described as very affordable and lifetime access once enrolled.

Q: What is the refund policy for the course?

The course offers a 30 day refund policy, allowing learners to try the videos and request a refund if they are not satisfied within 30 days.

Q: Who collaborated on the course material?

The course is a collaboration with Mayank Agarwal as part of Krishna’s team, focusing on understanding requirements for entering the generative AI agentic space and teaching how to use AutoGen effectively.

Q: What tools or platforms besides AutoGen are discussed in the course?

The content mentions AutoGen Studio as part of the tooling, and discusses configuring the AutoGen framework, building custom agents, chat bots, and deploying multi agent workflows across real world tasks.

Summary & Key Takeaways

• The course focuses on using Microsoft AutoGen to create autonomous agents and agentic AI systems, including setup, configuration, and deployment. It emphasizes practical, project based learning with real world workflows and collaboration between agents.

• It covers prerequisites like Python, and includes topics such as human in the loop, multi agent collaboration, and performance metrics to evaluate agent behavior and results.

• Users learn to design and implement custom agents and chat bots, and to deploy complex end to end tasks in production environments.