AutoGen Studio: No Code AI Agents

Name: AutoGen Studio: No Code AI Agents
Rating: 4.4 (209 reviews)

No-code AI automation with AutoGen Studio: agent orchestration, tool integration, workflow deployment,and real-world app

Created byMohsen Hassan, Ilyass Tabiai

Last updated 7/2025

English

English [Auto],Dutch [Auto],

What you'll learn

Learn about Agentic Oriented Programming in a no-code fashion
Become an expert at AutogenStudio (Microsoft)
Identify and act on issues that require AI to be solved
Develop Teams of AI agents able to undertake complex multi-step tasks

Course content

4 sections • 23 lectures • 5h 10m total length

Intro to LLMs6:37
Discover how large language models function as sophisticated autocompletes, leverage context windows, and use prompt engineering and ai agents to break tasks into targeted subtasks.
Agents4:51
Define agents as instances of a large language model with system messages, descriptions, names, and tools, enabling calculators, internet access, and code that agents can write themselves to perform tasks.
Teams2:42
Harness the power of AI agents by forming teams that break tasks into specialized roles, design, coding, testing, and documentation, using a waterfall model to deliver consistent results.
Round Robin Team3:14
Round robin teams involve multiple agents who work one after another, using shared chat history, until they complete the task, stopping via termination condition or a max messages limit.
Selector Team2:59
See how a selector team uses a selector agent with its own model and prompt to decide which agents should work next, enabling coordinated task flow.

AutoGen Studio Intro7:02
Autogen studio intro shows how to set up autogen studio to build agentic ai apps using language models, with a no-code interface, python tools, and both openai and local models.
Creating ChatGPT API12:16
Learn how autogen studio uses llms with OpenAI ChatGPT and local models, understand per token pricing and API key setup, and manage project creation and billing.
Installing LMStudio9:18
Install and run open source models locally with LM Studio to chat with llama on your machine, leveraging GPUs, RAM, and private computation.
Installing AutoGen Studio11:31
Install autogen studio by installing Python 3.12, creating a virtual environment, and installing autogen studio 0.4.2; then launch the UI to work with agents powered by OpenAI or local models.

AutoGen Studio Overview30:50
Explore the Autogen studio interface, load OpenAI or local models such as Llama 3.18b or Mistral 7b local, and build and deploy agent teams with a drag-and-drop team builder.
Round Robin Team Intro5:18
Explore the round robin theme in Autogen Studio, where three LLM agents chat in sequence, using termination conditions and max messages, producing either a complete or condensed summary.
Round Robin Team: Three Agents Example17:04
Explore creating a round robin team of three comedian agents in AutoGen Studio, configure system messages, termination conditions, and themes, and run with ChatGPT or a local llama model.
Selector Team Intro4:45
Explore the selector team in Autogen Studio, contrasting it with round-robin teams, where a ghost selector decides who speaks next based on chat history.
Selector Team: Creative vs Technical Writer14:50
Learn to build a selector team in Autogen Studio that routes requests to a creative or technical writer via a selector prompt, chat history, and agent descriptions.
Intro to Tools10:42
Learn how tools work in AutoGen Studio, using Python functions with annotations, hints, and docstrings to empower agents with no-code to low-code tools and calculators.
Tool Creator15:49
Create a two-team low-code workflow to generate Python tools and enable agents to use them, writing tools to disk and testing deployment in Autogen Studio.
Deploy your Team20:02
deploy your team with Autogen Studio to expose a no-code api on your machine, using a streamlit web app to connect to a creative versus technical writer team.

Websurfer Agent16:08
Explore Autogen Studio’s web surfer agent and its three-agent team (web server, verification assistant, user proxy) that browses the web, verifies results, and accepts user feedback.
Crypto News Team24:41
Build a no-code AI crypto news team with a news retriever using Crypto Panic API and a summarizer to convert API data into plain-language summaries.
Crypto Price & News Team17:10
Build a crypto price and news workflow using CoinGecko API to retrieve price, 24-hour change, and news, outputting JSON for automated content and thumbnails.
Selector Crypto Blog30:06
A selector team uses a writer and a critic to turn price and crypto news into a blog post, refine it, and generate a Dall-E thumbnail and markdown.
Lecture 22: Crypto News with RSS Feeds15:16
Use RSS feeds to fetch the latest crypto news via the Get Crypto News RSS tool, filter for the topic, and summarize with the Summarizer using an LLM.
Lecture 23: Connect Teams27:35
Demonstrates a waterfall model of multi-agent collaboration in Autogen Studio four, where two teams—designing and coding—pass outputs via tools to produce a blog post and thumbnail.

Requirements

No programming experience needed. You will learn everything in the course.

Description

Welcome to “AutoGen Studio: No Code AI Agents”, a comprehensive course designed to empower you with the skills to build advanced AI agent workflows without any programming. This course leverages Microsoft’s AutoGen Studio, enabling you to design, orchestrate, and deploy multi-agent AI solutions for a variety of real-world applications.

You will learn to:

Configure and manage agent teams for complex tasks
Integrate both cloud-based and local language models
Connect external tools and deploy agent workflows as APIs
Build practical projects such as a crypto blog generator

Throughout the course, you will engage in hands-on exercises and real-world projects that reinforce your understanding of each concept. You will also explore best practices for optimizing agent collaboration, troubleshooting common issues, and extending your workflows with additional tools and integrations.

This course is suitable for professionals, business users, and enthusiasts interested in AI automation, regardless of coding experience. All concepts are taught through step-by-step lectures, ensuring practical understanding and immediate applicability. By the end of the course, you will be proficient in using AutoGen Studio to create, deploy, and prototype no-code AI agent solutions.

Enroll now to unlock the potential of no-code AI development with AutoGen Studio 0.4.2 and transform your workflow automation capabilities.

Who this course is for:

Anyone curious about automating with AI

AutoGen Studio: No Code AI Agents

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 20min

Installation4 lectures • 40min

AutoGen Studio8 lectures • 1hr 59min

Autogen Studio Projects6 lectures • 2hr 11min

Requirements

Description

Who this course is for: