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.

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

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Building Multi-Agent Systems using LangGraph and Autogen

本课程是 Autonomous AI Agent Systems and Orchestration 专项课程的一部分

位教师：Edureka

包含在中

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4个模块

深入了解一个主题并学习基础知识。

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8 小时完成

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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.

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Autonomous AI Agent Systems and Orchestration 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有4个模块

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.

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.

涵盖的内容

12个视频5篇阅读材料4个作业

12个视频总计54分钟

Specialization Introduction4分钟
Course Introduction2分钟
From Static Content to Real-Time Financial Decisioning5分钟
Hands-on: The Observer Agent6分钟
Hands-on: Integrating Real-Time Data Sources4分钟
Hands-on: Data Validation and Pre-Processing for LLMs5分钟
Hands-on: Building the Fundamental Analysis Tool3分钟
Hands-on: Creating the Execution Tool with Strict Schema Guardrails3分钟
Hands-on: Using Multiple Tools in a Single Reasoning Step6分钟
Hands-on: RAG for Financial Knowledge - Indexing SEC Filings and Reports5分钟
Hands-on: Handling Numerical Data in RAG2分钟
Hands-on: The Research Agent 3分钟

5篇阅读材料总计50分钟

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

4个作业总计33分钟

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

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.

涵盖的内容

10个视频4篇阅读材料4个作业

10个视频总计55分钟

Beyond Single Agent: Principles of Collaborative Agent Teams5分钟
Hands-on: Designing Agent Roles - Researcher, Analyst, and Portfolio Manager6分钟
Hands-on: Setting Up the Orchestrator Agent4分钟
Hands-on: Communication Protocols Enabling Agents to Pass Structured Messages6分钟
Hands-on: The Analyst Agent's Role4分钟
Hands-on: Transitions Between Different Agents6分钟
Hands-on: Implementing a Consensus Mechanism for Investment Decisions5分钟
Hands-on: Generating a Trading Signal4分钟
Hands-on: The Full Collaborative Analysis 5分钟
Hands-on: Integrating Autogen and Gemini in Existing Workflow6分钟

4篇阅读材料总计40分钟

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

4个作业总计33分钟

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

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.

涵盖的内容

10个视频4篇阅读材料4个作业

10个视频总计56分钟

The Irrevocable Action Problem: Guardrails for Financial Execution5分钟
Hands-on: Implementing Pre-Execution Checks6分钟
LLM Jailbreak Prevention: Techniques to Stop Unauthorized Actions4分钟
Hands-on: Logging Every Thought and Action for Compliance5分钟
Hands-on: The Emergency Stop Node4分钟
Hands-on: Forcing Decisions on Strict Market Deadlines5分钟
Hands-on: Integrating the Final Review Queue for Execution6分钟
Hands-on: Packaging the Multi-Agent System for Containerization6分钟
Hands-on: Deploying the Autonomous Trading Agent API7分钟
Scaling Real-Time Systems and Advanced Portfolio Management4分钟

4篇阅读材料总计40分钟

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

4个作业总计33分钟

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

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.

涵盖的内容

1个视频1篇阅读材料2个作业

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位教师

Edureka

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Edureka

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常见问题

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.

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.