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( 10 Students )
AutoGen: Multi-Agent Conversational AI Framework
Build powerful LLM-based multi-agent systems with AutoGen—design, orchestrate, and deploy autonomous agents that talk, think, and solve problems.
Course Duration: 10 Hours
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AutoGen – Multi-Agent Conversational AI Framework – Online Course
AutoGen: Multi-Agent Conversational AI Framework is an advanced, self-paced online course tailored for AI engineers, researchers, and developers aiming to master the creation of multi-agent LLM ecosystems. Built by Microsoft Research, AutoGen is a Python-based framework that facilitates conversational orchestration of LLM agents, enabling collaborative reasoning, self-correction, tool use, and goal-oriented task execution.
This course offers the practical foundations, architectural understanding, and hands-on skills to build agent-to-agent workflows that mimic human-like dialogue, teamwork, and iterative problem-solving.
Course Introduction
As LLMs evolve beyond one-shot prompts and simple chats, the future lies in autonomous agents that communicate and collaborate to solve complex problems. AutoGen is a next-gen framework that enables just that—letting multiple agents, each with unique roles, memory, and capabilities, talk to one another, reason step by step, and make decisions.
As LLMs evolve beyond one-shot prompts and simple chats, the future lies in autonomous agents that communicate and collaborate to solve complex problems. AutoGen is a next-gen framework that enables just that—letting multiple agents, each with unique roles, memory, and capabilities, talk to one another, reason step by step, and make decisions.
With AutoGen, you can prototype use cases like code reviewers, researcher-bots, autonomous debugging agents, data analysts, and document analyzers that work together—often outperforming single-agent solutions.
What is AutoGen?
AutoGen is an open-source multi-agent framework that enables dynamic conversations between agents powered by LLMs. Each agent can act as a role-specific persona (e.g., Python coder, QA tester, planner), with memory, tool access, and response logic. AutoGen makes it easy to define inter-agent workflows, execute multi-turn reasoning, invoke tools, and even include humans in the loop.
AutoGen is an open-source multi-agent framework that enables dynamic conversations between agents powered by LLMs. Each agent can act as a role-specific persona (e.g., Python coder, QA tester, planner), with memory, tool access, and response logic. AutoGen makes it easy to define inter-agent workflows, execute multi-turn reasoning, invoke tools, and even include humans in the loop.
How to Use This Course
To make the most of this course:
To make the most of this course:
- Start with single-agent examples, then progress to multi-agent setups.
- Follow the hands-on labs and real projects, designing custom agents with memory and planning logic.
- Debug workflows using trace logs, agent logs, and conversational state diagrams.
- Use integrations with OpenAI, Azure OpenAI, and local models for flexibility.
- Explore advanced use cases including autonomous debugging and team-based research agents.
By the end, you’ll be able to design your own ecosystem of AI agents that can perform cooperative, robust, and scalable tasks in various domains.
Course Objectives Back to Top
By the end of this course, you will be able to:
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Understand the architecture and design principles behind AutoGen.
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Define and configure intelligent agents with roles, memory, and behavior.
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Build multi-agent systems capable of collaborative problem solving.
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Orchestrate agent conversations using conversation graphs.
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Integrate external tools, APIs, and user feedback in agent flows.
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Implement human-in-the-loop workflows using AutoGen’s UI agents.
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Deploy AutoGen applications with OpenAI, Azure, or local LLMs.
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Debug and monitor agent conversations using tracing and logs.
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Apply multi-agent logic to real-world scenarios like code review or research bots.
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Follow best practices in safety, prompt chaining, and agent alignment.
Course Syllabus Back to Top
Course Syllabus
Module 1: Introduction to AutoGen
- What is AutoGen?
- Single-agent vs multi-agent LLM design
- Core use cases and system architecture
Module 2: Installing and Exploring AutoGen
- Setting up AutoGen (local + cloud)
- Required packages and LLM providers
- Anatomy of an agent conversation
Module 3: Creating Your First Agent
- Basic agent definition and configuration
- Roles, objectives, and memory
- Running simple agent tasks
Module 4: Building a Multi-Agent System
- Creating multiple agents with different personas
- Defining goals, stopping criteria, and interactions
- Structuring inter-agent dialogues
Module 5: Tools and Function Calling
- Built-in tool use and plugin system
- Invoking Python functions from agents
- Calling APIs and processing outputs
Module 6: Conversation Orchestration
- Role assignment and message flow
- Using GroupChat and AutoGen’s controller
- Conditional task switching
Module 7: Human-Agent Collaboration
- Introducing HumanProxyAgent
- Taking user input in a conversation loop
- Real-time interaction and override
Module 8: Tracing, Debugging & Logs
- Using observe functions to track messages
- Conversation history and performance
- Visualizing the chat flow
Modules 9–11: Real Projects
- Project 1: Multi-Agent Code Debugger (Dev + Tester)
- Project 2: Research Assistant Team (Planner + Researcher + Writer)
- Project 3: Autonomous Data Pipeline Builder
Module 12: Deploying AutoGen Systems
- Running on cloud VMs or containers
- Using AutoGen with streamlit or Gradio
- Scheduling agent runs via APIs
Module 13: Agent Safety, Prompt Design, and Evaluation
- Guardrails for autonomous reasoning
- Prompt templates and behavior shaping
- Metrics for evaluating agent collaboration
Module 14: AutoGen Interview Questions & Answers
Certification Back to Top
Upon successful completion of the AutoGen: Multi-Agent Conversational AI Framework course, learners will receive a Certificate of Completion from Uplatz, verifying their expertise in developing multi-agent LLM systems using AutoGen. The certification demonstrates advanced skills in AI orchestration, cooperative agent design, prompt engineering, and real-world LLM application architecture. It is ideal for AI engineers, researchers, automation experts, and innovators exploring the future of intelligent agents.
Career & Jobs Back to Top
AutoGen represents the cutting edge of autonomous AI agent design. As companies and researchers seek to create scalable, explainable, and capable AI systems, professionals who can design and manage multi-agent LLM frameworks are increasingly valuable.
This course opens the door to roles such as:
- Multi-Agent Systems Engineer
- AI Orchestration Developer
- LLM Research Engineer
- Conversational AI Architect
- Applied AI Scientist
- Prompt and Agent Designer
Opportunities exist in AI startups, research labs, enterprise R&D, dev tooling companies, and autonomous software ventures. With AutoGen skills, you’ll be positioned to shape the next era of AI-driven automation and team-based intelligence systems.
Interview Questions Back to Top
1. What is AutoGen and how does it differ from other LLM frameworks?
AutoGen is a multi-agent orchestration framework from Microsoft that allows agents to talk to each other using natural language and complete tasks collaboratively, unlike single-agent prompt frameworks.
AutoGen is a multi-agent orchestration framework from Microsoft that allows agents to talk to each other using natural language and complete tasks collaboratively, unlike single-agent prompt frameworks.
2. How are agents defined in AutoGen?
Each agent is a Python class with specific configurations like name, system message, LLM model, memory, and behavior callbacks.
Each agent is a Python class with specific configurations like name, system message, LLM model, memory, and behavior callbacks.
3. What are some use cases for AutoGen?
Code debugging, research collaboration, document summarization, autonomous planning, and developer team simulations are common use cases.
Code debugging, research collaboration, document summarization, autonomous planning, and developer team simulations are common use cases.
4. What is GroupChat in AutoGen?
GroupChat enables structured conversations between multiple agents, controlling who talks when and how the chat progresses based on logic.
GroupChat enables structured conversations between multiple agents, controlling who talks when and how the chat progresses based on logic.
5. Can AutoGen integrate human feedback in loops?
Yes, using the HumanProxyAgent, you can integrate real-time human input as part of the agent conversation or decision-making process.
Yes, using the HumanProxyAgent, you can integrate real-time human input as part of the agent conversation or decision-making process.
6. How does tool calling work in AutoGen?
Agents can invoke Python functions or external APIs when equipped with tool access, enabling them to retrieve data or perform actions during reasoning.
Agents can invoke Python functions or external APIs when equipped with tool access, enabling them to retrieve data or perform actions during reasoning.
7. What LLMs can you use with AutoGen?
AutoGen supports OpenAI, Azure OpenAI, Hugging Face, and local models like Llama via LangChain or direct API integration.
AutoGen supports OpenAI, Azure OpenAI, Hugging Face, and local models like Llama via LangChain or direct API integration.
8. How do you monitor agent conversations in AutoGen?
You can observe and log messages using callback hooks, trace message history, and analyze inter-agent communication using built-in utilities.
You can observe and log messages using callback hooks, trace message history, and analyze inter-agent communication using built-in utilities.
9. What are challenges in designing multi-agent systems?
Controlling message flow, avoiding infinite loops, defining clear roles, and handling failure cases are key design challenges.
Controlling message flow, avoiding infinite loops, defining clear roles, and handling failure cases are key design challenges.
10. How do you evaluate agent performance in AutoGen?
Using feedback functions, conversation length, goal completion rates, and log inspection, you can evaluate the success and efficiency of agent tasks.
Using feedback functions, conversation length, goal completion rates, and log inspection, you can evaluate the success and efficiency of agent tasks.
Course Quiz Back to Top
FAQs
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