Overview

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. Through guided lessons and coding demonstrations, you’ll learn how to construct multiple AI agents that communicate, plan, and execute tasks autonomously. You will work with LangGraph to structure agent workflows and use Autogen to enable dynamic interaction between agents. The course covers key topics such as agent communication, reasoning loops, task decomposition, and coordination for real-world applications like research, analysis, and workflow management. By the end of this course, you will be able to: • Understand the architecture, behavior, and lifecycle of multi-agent systems. • Build intelligent agents using LangGraph and Autogen for collaborative problem-solving. • Implement reasoning and communication strategies for effective task orchestration. • Evaluate and optimize multi-agent performance for scalability and reliability. This course is ideal for developers, data scientists, and AI practitioners who want to learn how to design and deploy intelligent multi-agent systems that can perform complex workflows autonomously. A basic understanding of Python programming and familiarity with machine learning or AI concepts will be helpful, but no prior experience with LangGraph or Autogen is required. Join us to explore the future of autonomous AI systems and learn how to build, coordinate, and optimize agents that think, collaborate, and act intelligently.

Syllabus

Real-Time Data and Advanced Tooling

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.

Multi-Agent Collaboration and Decision-Making

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.

Security, Auditability, and Deployment

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.

Course Wrap-Up and Assessment

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.