AutoGen Essentials

Course Overview

Intermediate

Free Course

For engineers preparing for AI agent interviews that ask about Microsoft AutoGen and multi-agent systems. You will be able to explain AutoGen's agents, teams, tools, memory and orchestration patterns in both the current 0.4+ API and the legacy 0.2 API, and answer questions on evaluation, safety and where AutoGen stands next to Microsoft Agent Framework and AG2 in 2026.

Instructor: MantraMindAI

Sections: 7

Course Content

Section 1: AutoGen Basics

Prepares you to explain what AutoGen is, its building blocks in the 0.4+ and legacy 0.2 APIs, its main agent types, and when to pick it over LangGraph, CrewAI or Microsoft Agent Framework in 2026.

1. What is Microsoft AutoGen, and what problems does it solve compared to single-agent chatbots?

2. What are AutoGen’s core components—agents, messages, group chat—and how do they interact?

3. What’s the difference between an AssistantAgent, UserProxyAgent, and tool-enabled agent in AutoGen?

4. When would you choose AutoGen over LangChain/LangGraph or CrewAI?

Section 2: Multi-Agent Conversation Design

Prepares you to explain how to write agent roles that do not overlap, stop agents from looping, pick the right number of agents, and control turn-taking and stopping in AutoGen teams.

1. How do you define agent roles and prompts so multiple agents avoid overlap and endless conflict?

2. What strategies do you use to prevent infinite loops or repetitive agent-to-agent chatter?

3. How do you decide the right number of agents for a workflow (2 vs 3 vs many)?

4. How do you implement turn-taking and stopping conditions in AutoGen group chats?

Section 3: Tools, Function Calling, and Execution

Prepares you to explain how AutoGen agents get and use tools, how to scope what each agent may call, how to run generated code safely, and how to handle tool failures without loops or made-up results.

1. How do you register tools/functions in AutoGen and control what each agent is allowed to call?

2. How do you make an AutoGen agent decide when to call a tool vs respond directly?

3. How do you sandbox code execution safely when using a UserProxyAgent (or executor agent)?

4. How do you handle tool failures (timeouts, bad inputs, partial outputs) in an agent workflow?

Section 4: Memory and State Management

Prepares you to explain short-term and long-term memory in AutoGen, how to save and resume agent state across sessions, how to keep context windows small, and how to stop bad memories from steering decisions.

1. How do you manage short-term vs long-term memory in AutoGen conversations?

2. How do you persist agent state across sessions (conversation logs, summaries, vector memory)?

3. What are effective strategies to keep context windows small while maintaining continuity?

4. How do you prevent “memory poisoning” or irrelevant memories from harming decisions?

Section 5: Orchestration Patterns

Prepares you to explain how to build planner–executor and writer–critic teams in AutoGen, run agents in parallel and merge their work, route tasks to the right agent, and add reflection loops that pay for themselves.

1. How do you implement agent patterns in AutoGen like planner–executor or researcher–writer–critic?

2. How do you do parallel work in AutoGen (e.g., multiple researchers) and merge outputs reliably?

3. How do you route tasks dynamically to different agents based on intent or confidence?

4. How can you add reflection or self-critique loops without wasting tokens or overfitting feedback?

Section 6: Evaluation and Reliability

Prepares you to explain how to measure a multi-agent AutoGen system, build golden tasks and regression tests, catch hallucinated decisions and tool arguments, and judge whether agents are actually collaborating well.

1. How do you evaluate a multi-agent AutoGen system (success rate, tool accuracy, latency, cost)?

2. How do you build “golden tasks” and regression tests for agent workflows?

3. How do you detect and reduce hallucinations in agent-produced decisions and tool arguments?

4. How do you measure collaboration quality (handoffs, conflict resolution, redundancy)?

Section 7: Safety and Security

Prepares you to explain the main security risks of AutoGen multi-agent systems and how to answer them with least-privilege tools, human approval on risky actions, and containment of indirect prompt injection.

1. What are the main security risks in AutoGen multi-agent systems, such as prompt injection and data leaks?

2. How do you enforce least-privilege tool access per agent?

3. How do you implement human-in-the-loop approvals for high-risk actions?

4. How do you protect against indirect prompt injection when agents browse the web or read documents?