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Multi-Agent Development with AutoGen

Multi-Agent Development with AutoGen

4.5 (164 ratings)
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Multi-Agent Development with AutoGen

Multi-Agent Development with AutoGen involves building groups of AI tools, or "agents," that can interact and collaborate to get work done. Instead of having just one AI doing everything, you create several agents—each with its own role—and AutoGen helps them work as a team. This makes it easier to handle complicated tasks by dividing the work among different agents.

AutoGen simplifies the process by letting developers set up communication between agents, assign specific goals, and watch how they cooperate. These systems can be used in real-life situations like organizing data, assisting users, or running simulations. It’s a smart way to build AI setups that are more efficient and capable than one tool working alone.

Who should take the Exam?

This exam is ideal for:

  • AI/ML engineers and developers
  • Automation professionals
  • Software engineers exploring AI tooling
  • Data scientists looking to automate workflows
  • RPA (Robotic Process Automation) engineers
  • Tech leads in AI startups
  • Product managers building AI-driven apps
  • Researchers interested in agent-based systems

Skills Required

  • Basic Python programming
  • Familiarity with APIs and AI model usage
  • Understanding of LLMs (Large Language Models)
  • Knowledge of prompt engineering
  • Experience with task automation or workflow design is a plus

Course Outline

Domain 1 - Introduction to Multi-Agent Systems

Domain 2 - Overview of AutoGen

Domain 3 - Defining and Configuring Agents

Domain 4 - Agent Communication and Coordination

Domain 5 - Integrating Tools and External APIs

Domain 6 - Building Workflows with AutoGen

Domain 7 - Testing and Debugging Agents

Domain 8 - Best Practices for Agent Design

Domain 9 - Ethical and Secure Agent Systems

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4.5

(Based on 164 reviews)

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Multi-Agent Development with AutoGen FAQs

Examples include automated report generators, multi-agent customer service bots, collaborative code assistants, and intelligent workflow systems.

Agent creation, task delegation, agent communication, planning workflows, using LLM APIs, integrating tools (APIs, code execution), and debugging agents.

Yes. A solid grasp of Python and experience working with AI/LLM APIs is important to effectively build and orchestrate agents using AutoGen.

Finance, healthcare, enterprise automation, research, logistics, customer service, and any field moving toward intelligent automation.

It demonstrates advanced AI engineering capabilities and familiarity with next-gen agent orchestration tools — a growing area of interest in tech and enterprise AI.

Yes. It equips you to build innovative AI-driven products and services where autonomous or collaborative AI agents provide strategic value.

It involves designing and deploying intelligent systems where multiple AI agents collaborate or operate autonomously using the AutoGen framework.

 

AI Engineer, Autonomous Systems Developer, AI Researcher, Solutions Architect, and Innovation Technologist.

They enable more scalable, autonomous, and flexible AI solutions — critical for complex workflows like automated research, business process automation, and decision-making systems.

AutoGen is an open framework developed by Microsoft that allows the orchestration of multiple LLM-based agents to perform complex tasks collaboratively.

Yes. It builds practical, transferable skills that are directly applicable in both open-source contributions and enterprise AI development.

AI developers, ML engineers, software engineers, researchers, and advanced data professionals interested in cutting-edge AI automation and agent design.

Absolutely. It opens doors to research in collaborative AI, agent communication, LLM orchestration, and AI planning.

AutoGen emphasizes multi-agent orchestration and LLM coordination, with a unique conversational interface and dynamic control over workflows.

Very. As AI evolves, multi-agent systems are expected to become foundational for autonomous reasoning, task management, and scalable decision-making.