What kinds of tools will I learn to build?

You’ll learn how to create tools that help your agent search the web, fetch information, and log activities. This includes connecting your agent to simple apps and databases.

What is RAG, and why is it useful?

RAG (Retrieval-Augmented Generation) helps your agent find and use real information from documents, so its answers are accurate and reliable.

Will I learn how to make my AI follow workflows or rules?

Yes! You’ll see how to map out tasks step by step, check the AI’s work, and even include manual approvals when needed, making your agent smart and reliable.

Can I make my agent work in real life or online?

Absolutely. You’ll learn to package your agent as an online service (API) and monitor it so it can run safely and scale to handle real tasks.

How will I be tested in this course?

Each module has short quizzes to check understanding and practical exercises where you build and test your agent, so you can apply what you’ve learned in real scenarios.

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 Autonomous AI Agents

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Building Autonomous AI Agents

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。

初级等级

推荐体验

7 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。

初级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Understand the core architecture and components of autonomous AI agents.
Build and orchestrate multi-agent systems using frameworks.
Integrate memory, reasoning, and tool-use capabilities into AI workflows.
Apply ethical, safety, and evaluation frameworks for trustworthy agent deployment.

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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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通过实践项目培养工作相关技能
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该课程共有4个模块

Artificial intelligence is rapidly advancing from simple automation to systems that can reason, plan, and act on their own. Building Autonomous AI Agents is a hands-on, practice-driven course that walks you through the end-to-end process of designing, developing, and deploying intelligent agents capable of independent decision-making.

You’ll work with leading agent frameworks—including LangChain, Autogen, and AgentOps—and learn how to integrate models, tools, and APIs to build dynamic multi-agent ecosystems. Through guided demonstrations and structured labs, you’ll implement core agent components such as memory, tool use, planning modules, and goal-driven workflows, ultimately building a fully functional autonomous agent. The course also emphasizes safety, evaluation, and alignment practices to ensure that agents operate transparently, ethically, and reliably in real-world settings. By the end of this course, you will be able to: • Understand core agent architectures, memory types, and planning strategies. • Build agents using frameworks like LangChain, CrewAI, Autogen, and AgentOps. • Connect models, APIs, and external tools into cohesive multi-agent systems. • Implement goal-driven workflows with monitoring, evaluation, and safety controls. • Deploy production-ready autonomous agents capable of operating reliably in real environments. This course is ideal for AI developers, data scientists, software engineers, and technology professionals transitioning from basic prompt engineering to building fully autonomous systems. A foundational understanding of Python, APIs, and basic AI concepts is recommended, though all frameworks are introduced from scratch. Join us to master the tools and techniques that power the next generation of AI systems that can think, act, and collaborate with minimal human intervention.

This module introduces learners to the foundations of single AI agents using the ReAct framework. Learners will explore the core concepts of agentic reasoning, tool usage, and memory integration. Through hands-on exercises, they will set up a development environment, define and use tools with structured inputs, and implement the ReAct loop for reasoning and decision-making. By the end of this module, learners will be able to deploy a functional agent capable of performing tasks with structured reasoning and short-term memory.

涵盖的内容

15个视频6篇阅读材料4个作业

15个视频总计59分钟

Specialization Introduction4分钟
Course Introduction4分钟
Python Prerequisites for Agentic AI Development5分钟
Setting Up Environment for AI Agents3分钟
The Rise of Autonomous Agents and the SDR Case Study4分钟
AI Agent Protocols3分钟
Agent vs. LLM Why ReAct is the Next Generation of Prompting4分钟
Hands-On: Setting Up the Agent Development Environment (Python & Libraries)3分钟
Hands-On: Anatomy of a Tool (Part 1) Defining Functions & Docstrings6分钟
Hands-On: Anatomy of a Tool (Part 2) Using Pydantic for Structured Input3分钟
Hands-On: Building Your First Research Tool (Web Search Integration)3分钟
The Core ReAct Loop: Thought, Action, Observation in Code3分钟
Hands-On: Prompt Engineering for Reasoning How to Get Better Tool Use3分钟
Hands-On: Implementing State Giving Your Agent Short-Term Memory3分钟
Hands-On: Milestone Deploying the Researcher Agent (Tool & Memory Test)3分钟

6篇阅读材料总计60分钟

Course Outline: Building Autonomous AI Agents 10分钟
Understanding the ReAct Reasoning Loop10分钟
Foundations of Agentic AI10分钟
Tool Development and Action10分钟
ReAct Implementation & Memory10分钟
Summary: The Agentic Foundation (ReAct & Tool Use)10分钟

4个作业总计33分钟

Practice Quiz: Foundations of Agentic AI6分钟
Practice Quiz: Tool Development and Action6分钟
Practice Quiz: ReAct Implementation & Memory6分钟
Knowledge Check: The Agentic Foundation (ReAct & Tool Use)15分钟

This module focuses on enabling a single agent to access, process, and act on external knowledge. Learners will work with retrieval-augmented generation (RAG) pipelines, including data ingestion, text embedding, and vector database indexing. They will integrate tools and actuators to enable decision-making and apply grounding techniques to ensure the agent produces contextually accurate outputs. By the end of this module, learners will have built a “strategy-grounded” agent that can reason over knowledge sources and generate validated outputs.

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31分钟

RAG Theory3分钟
Hands-On: Data Preparation Loading & Splitting the Internal Sales Playbook3分钟
Hands-On: Text Embeddings How to Convert Documents to Vector Space3分钟
Hands-On: Building the Vector Database & Indexing the Sales Playbook3分钟
Hands-On: Creating the RAG Retrieval Tool (retrieve_playbook)3分钟
Hands-On: Connecting to the CRM Defining the Actuator Tool (log_activity)3分钟
Hands-On: LLM Decisioning When to Use the Web Tool vs. the RAG Tool2分钟
The Multi-Step Chain: Sequencing Research, RAG, and Drafting3分钟
Hands-On: Prompting for Grounding and Forcing the Agent to Cite the Playbook2分钟
Hands-On: The Strategy-Grounded Draft Agent (Full Output Test)3分钟

4篇阅读材料总计40分钟

RAG Architecture and Data Ingestion10分钟
Tool Integration and Actuators10分钟
Strategy-Grounded Drafting10分钟
Summary: Context, Knowledge, and Grounding (RAG)10分钟

4个作业总计33分钟

Practice Quiz: RAG Architecture and Data Ingestion6分钟
Practice Quiz: Tool Integration and Actuators6分钟
Practice Quiz: Strategy-Grounded Drafting6分钟
Knowledge Check: Context, Knowledge, and Grounding (RAG)15分钟

This module introduces learners to orchestrating, validating, and deploying single AI agents using LangGraph. Learners will design execution graphs, implement validation nodes, and integrate reflection loops for self-correction. They will also explore human-in-the-loop techniques and conditional logic for decision-making. Finally, learners will package their agent as a RESTful API, monitor its performance, and scale workflows for robust operation. By the end of this module, learners will have a fully operational, production-ready agent capable of autonomous task execution.

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37分钟

Graph Theory and Motivation3分钟
Anatomy of a Graph: Nodes, Edges, and Defining Graph State3分钟
Hands-On: Mapping the SDR Workflow Designing the Full Execution Graph2分钟
Hands-On: Building the Validation Node Creating the LLM-as-a-Judge2分钟
Hands-On: Implementing the Reflection Loop Routing for Self-Correction3分钟
Hands-On: Conditional Edges and the Logic of Decision-Making in the Graph4分钟
Hands-On: Integrating Manual Approval into the Workflow3分钟
Hands-On: Finalizing the Actuator and Ensuring Atomic CRM Logging3分钟
Hands-On: Packaging the Agent as a RESTful API with FastAPI3分钟
Hands-On: Monitoring, Scaling, and Advanced Multi-Agent Systems5分钟

4篇阅读材料总计40分钟

Building the State Machine10分钟
Advanced Control and Self-Correction10分钟
Productionizing the Agent10分钟
Summary: Orchestration, Validation, and Deployment (LangGraph)10分钟

4个作业总计33分钟

Practice Quiz: Building the State Machine6分钟
Practice Quiz: Advanced Control and Self-Correction6分钟
Practice Quiz: Productionizing the Agent6分钟
Knowledge Check: Orchestration, Validation, and Deployment (LangGraph)15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 agentic foundations, RAG pipelines, 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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常见问题

AI agents can not only chat but also take actions, use tools, and remember things to complete tasks. Regular AI models mainly generate text without acting or remembering.

You only need basic Python knowledge. Step-by-step instructions are provided for all coding exercises, so beginners can follow along easily.

ReAct stands for Reasoning + Acting. It teaches agents to think, take action, observe results, and improve, making them smarter than standard AI models.

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.