What is the main goal of this course?

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.

What prior knowledge is required?

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.

What tools or frameworks are used in the course?

The course primarily uses LangGraph for agent orchestration, LLMs for reasoning and communication, RAG for financial data retrieval.

How does this course differ from standard AI or data science courses?

Unlike typical data analysis courses, this one focuses on autonomous decision systems — where multiple AI agents operate collaboratively, process real-time inputs, and make market-driven choices securely and audibly.

What practical outcomes can I expect?

By the end of the course, learners will have built a multi-agent financial system capable of real-time data ingestion, collaborative analysis, signal generation, and safe deployment in production-like conditions.

How is security and compliance addressed in the course?

Security is a key focus. Learners implement guardrails, pre-execution checks, audit logs, and jailbreak prevention mechanisms to ensure all agent actions are safe, compliant, and transparent.

What are the main assessment types?

Each module concludes with ungraded hands-on exercises and quizzes, followed by a graded module quiz assessing understanding of real-time tooling, multi-agent workflows, and deployment best practices

What real-world applications does this training prepare me for?

The course prepares learners for roles in AI-driven finance, algorithmic trading, autonomous analytics, and enterprise agent design, where AI systems must process dynamic data securely and collaboratively.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Building Multi-Agent Systems using LangGraph and Autogen

This course is part of Autonomous AI Agent Systems and Orchestration Specialization

Instructor: Edureka

Included with

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

4 modules

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

AI Security

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

14 assignments¹

AI Graded see disclaimer

Taught in English

91% of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Autonomous AI Agent Systems and Orchestration Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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.

Module details

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

12 videosTotal 55 minutes

Specialization Introduction5 minutes
Course Introduction2 minutes
From Static Content to Real-Time Financial Decisioning5 minutes
Hands-on: The Observer Agent7 minutes
Hands-on: Integrating Real-Time Data Sources4 minutes
Hands-on: Data Validation and Pre-Processing for LLMs6 minutes
Hands-on: Building the Fundamental Analysis Tool4 minutes
Hands-on: Creating the Execution Tool with Strict Schema Guardrails4 minutes
Hands-on: Using Multiple Tools in a Single Reasoning Step7 minutes
Hands-on: RAG for Financial Knowledge - Indexing SEC Filings and Reports5 minutes
Hands-on: Handling Numerical Data in RAG3 minutes
Hands-on: The Research Agent 3 minutes

5 readingsTotal 50 minutes

Course Outline: Building Multi-Agent Systems using LangGraph and Autogen10 minutes
Foundations of Real-Time Agents10 minutes
Tool Ensemble Design10 minutes
RAG for Financial Data10 minutes
Real-Time Data and Advanced Tooling10 minutes

4 assignmentsTotal 33 minutes

Knowledge Check: Real-Time Data and Advanced Tooling15 minutes
Practice Quiz: Foundations of Real-Time Agents6 minutes
Practice Quiz: Tool Ensemble Design6 minutes
Practice Quiz: RAG for Financial Data6 minutes

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

10 videosTotal 55 minutes

Beyond Single Agent: Principles of Collaborative Agent Teams5 minutes
Hands-on: Designing Agent Roles - Researcher, Analyst, and Portfolio Manager6 minutes
Hands-on: Setting Up the Orchestrator Agent4 minutes
Hands-on: Communication Protocols Enabling Agents to Pass Structured Messages6 minutes
Hands-on: The Analyst Agent's Role4 minutes
Hands-on: Transitions Between Different Agents6 minutes
Hands-on: Implementing a Consensus Mechanism for Investment Decisions5 minutes
Hands-on: Generating a Trading Signal5 minutes
Hands-on: The Full Collaborative Analysis 6 minutes
Hands-on: Integrating Autogen and Gemini in Existing Workflow7 minutes

4 readingsTotal 40 minutes

The Multi-Agent Architecture10 minutes
Collaborative Analysis Workflow10 minutes
Signal Generation and Risk10 minutes
Multi-Agent Collaboration and Decision-Making10 minutes

4 assignmentsTotal 33 minutes

Knowledge Check: Multi-Agent Collaboration and Decision-Making15 minutes
Practice Quiz: The Multi-Agent Architecture6 minutes
Practice Quiz: Collaborative Analysis Workflow6 minutes
Practice Quiz: Signal Generation and Risk6 minutes

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

10 videosTotal 57 minutes

The Irrevocable Action Problem: Guardrails for Financial Execution5 minutes
Hands-on: Implementing Pre-Execution Checks6 minutes
LLM Jailbreak Prevention: Techniques to Stop Unauthorized Actions5 minutes
Hands-on: Logging Every Thought and Action for Compliance6 minutes
Hands-on: The Emergency Stop Node4 minutes
Hands-on: Forcing Decisions on Strict Market Deadlines5 minutes
Hands-on: Integrating the Final Review Queue for Execution7 minutes
Hands-on: Packaging the Multi-Agent System for Containerization7 minutes
Hands-on: Deploying the Autonomous Trading Agent API8 minutes
Scaling Real-Time Systems and Advanced Portfolio Management5 minutes

4 readingsTotal 40 minutes

Production-Grade Security & Guardrails10 minutes
Advanced LangGraph Control10 minutes
Deployment and Scaling 10 minutes
Security, Auditability, and Deployment10 minutes

4 assignmentsTotal 33 minutes

Knowledge Check: Security, Auditability, and Deployment15 minutes
Practice Quiz: Production-Grade Security & Guardrails6 minutes
Practice Quiz: Advanced LangGraph Control6 minutes
Practice Quiz: Deployment and Scaling6 minutes

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.