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Coordinating Intelligence: Architecting Multi-Agent Systems

IntermediateGuided Project

Ready to create AI that doesn't just think—but collaborates? In this project you will build multi-agent systems using frameworks like LangGraph and LangChain where autonomous agents team up, share intel, and coordinate like a well-oiled machine. You will employ agentic design patterns, and strategies that make agents robust enough for the real world.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • Generative AI, Agentic AI, LangGraph, LangChain

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 90 minutes

Platform

  • SkillsNetwork

Last Update

  • October 5, 2025
About this Guided Project
Why Multi-Agent Systems Matter
Ready to create AI that doesn't just think—but collaborates? Multi-agent systems are revolutionizing how we build intelligent applications by enabling autonomous agents to work together, share knowledge, and tackle complex real-world problems. This hands-on workshop will transform you from someone who knows about AI into someone who can architect intelligent, collaborative systems, with a special focus on the orchestrator—the master coordinator that manages agent interactions and workflows.

A Look at the Project Ahead
By completing this project, you'll be able to:
  • Build multi-agent systems using LangChain and LangGraph frameworks
  • Design and implement orchestrator patterns to coordinate multiple agents effectively
  • Implement Agentic design patterns for robust, scalable AI systems
  • Understand when to use agents vs. traditional LLMs and make informed architectural decisions
  • Create orchestration workflows that manage complex agent interactions and task distribution
  • Deploy collaborative AI systems that can handle real-world complexity
What You'll Need
  • Bring your own device (laptop recommended)
  • Basic Python programming knowledge
  • Familiarity with AI/ML concepts (helpful but not required)
  • Current version of Chrome, Edge, Firefox, or Safari
  • Enthusiasm for building intelligent systems that actually work together!
Note: The IBM Skills Network Labs environment comes pre-configured with the necessary tools and frameworks, so you can focus on learning rather than setup.

Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Joshua Zhou

Data Scientist

I like building fun and practical things.

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Jianping Ye

Data Scientist Intern at IBM

I'm Jianping Ye, currently a Data Scientist Intern at IBM and a PhD candidate at the University of Maryland. I specialize in designing AI solutions that bridge the gap between research and real-world application. With hands-on experience in developing and deploying machine learning models, I also enjoy mentoring and teaching others to unlock the full potential of AI in their work.

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Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

I am a data scientist and economist with a strong background in econometrics, time series analysis, causal inference, and statistics. I stand out for my ability to combine technical expertise with clear communication, turning complex data findings into practical insights for stakeholders at every level. Follow my projects to learn about data science principles, machine learning algorithms, and artificial intelligence agents.

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Tenzin Migmar

Data Scientist

Hi, I'm Tenzin. I'm a data scientist intern at IBM interested in applying machine learning to solve difficult problems. Prior to joining IBM, I worked as a research assistant on projects exploring perspectivism and personalization within large language models. In my free time, I enjoy recreational programming and learning to cook new recipes.

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