This course introduces the essentials of multi-agent AI systems using LangGraph and Autogen, combining architectural understanding with hands-on development of intelligent, collaborative agents. Designed to give you both conceptual foundations and practical experience, it explores how agent-based systems are redefining automation, decision-making, and AI-powered problem-solving.
Building Multi-Agent Systems using LangGraph and Autogen
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Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
8 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Design and build multi-agent systems that reason, plan, and collaborate on shared goals.
Implement communication and coordination strategies using LangGraph and Autogen.
Evaluate system performance through structured tasks and adaptive reasoning loops.
Optimize multi-agent workflows for reliability, scalability, and autonomous execution.
Skills you'll gain
Details to know
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Taught in English
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There are 4 modules in this course
This module explores how real-time data and advanced tooling empower autonomous agents to make dynamic financial decisions. You’ll learn to integrate live data sources, validate inputs, and build multi-tool ensembles for complex reasoning. Finally, you’ll apply RAG techniques to index, query, and analyze financial data in real time.
What's included
12 videos5 readings4 assignments
This module delves into multi-agent collaboration, where specialized agents work together to analyze data and make informed decisions. You’ll design coordinated agent roles and communication protocols for seamless teamwork. The module culminates in building a full collaborative workflow that generates trading signals and balances investment risk.
What's included
10 videos4 readings4 assignments
This module focuses on building secure, auditable, and scalable AI agent systems for real-world deployment. You’ll implement guardrails, logging, and fail-safes to ensure responsible financial execution. Finally, you’ll package, deploy, and scale your multi-agent trading system using production-ready infrastructure.
What's included
10 videos4 readings4 assignments
This module provides learners with an opportunity to synthesize their knowledge and demonstrate mastery of single-agent AI workflows. Learners will review key concepts from multi agent systems, , MCP and LangGraph orchestration. They will complete graded assessments, including scenario-based exercises and end-of-course knowledge checks, to apply their understanding in practical contexts. By the end of this module, learners will be able to confidently design, implement, and evaluate a fully functional single AI agent capable of reasoning, tool use, and executing grounded tasks.
What's included
1 video1 reading2 assignments
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Frequently asked questions
This course aims to teach how to design, build, and deploy autonomous financial agents capable of real-time decision-making, collaborative reasoning, and secure execution within live trading or analysis environments.
A foundational understanding of Python, APIs, and basic AI or LLM concepts is recommended. Familiarity with financial data or market terminology helps but is not mandatory.
The course primarily uses LangGraph for agent orchestration, LLMs for reasoning and communication, RAG for financial data retrieval.
More questions
Financial aid available,
¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.