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Building AI Agents for Complex Tasks

This course is part of Hands-on Agentic AI: Building Intelligent Agents Specialization

Instructor: Hurix Digital

2,459 already enrolled

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

This course is part of the Hands-on Agentic AI: Building Intelligent Agents 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 3 modules in this course

Building AI Agents for Complex Tasks is an intermediate-level course designed to equip learners with the skills to design, build, and evaluate intelligent agents that operate autonomously across dynamic, multi-step environments. Moving beyond simple chatbot flows, this course introduces learners to agent architectures that perceive context, make decisions, integrate tools, and recover from failure.

Through hands-on labs, interactive video walkthroughs, and real-world case studies—including Alexa, BabyAGI, and AlphaCode—learners will explore agent types, design patterns, tool orchestration, memory management, and behavior evaluation. They'll gain practical experience using modern frameworks like LangChain and Rasa to construct agents for use cases such as research automation, virtual assistants, and decision-making bots. By the end of the course, learners will have built and tested their own intelligent agent and developed the foundational skills to implement agent-based AI systems that can adapt, reason, and act in real-world applications.

Module details

This foundational lesson introduces what AI agents are and how they differ from traditional software. Learners will explore agent-environment interactions, the concept of perception, and how various types of agents—reactive, deliberative, and hybrid—handle decision-making. Through real-world examples like smart assistants and warehouse robots, learners will classify agent types and determine where each model excels or breaks down.

What's included

4 videos1 reading1 assignment

4 videosTotal 18 minutes

Introduction and Welcome2 minutes
How AI Agents Perceive the World Around Them7 minutes
Reactive vs. Deliberative Agents in the Real World5 minutes
Rasa vs. AutoGPT – Choosing the Right Agent Model4 minutes

1 readingTotal 6 minutes

Welcome to the Course: Course Overview6 minutes

1 assignmentTotal 10 minutes

HOL: Practice Classifying Agent Types in Real-World Use Cases10 minutes

This lesson moves from theory to implementation. Learners will construct intelligent agents that integrate inputs (perception), structured reasoning (decision loops), and output (action). They'll explore core modules such as memory, planning chains, and tool execution in LangChain and Rasa. Real-world examples like Alexa’s task-based updates and LangChain agents with tools will help frame the technical walkthroughs.

What's included

3 videos1 reading2 assignments

3 videosTotal 15 minutes

How Intelligent Agents Turn Input into Impact5 minutes
Designing Action Loops and Memory Modules5 minutes
Alexa Multi-Intent Flow and Long Chain Tool Use5 minutes

1 readingTotal 8 minutes

LangChain and Rasa: Agent Workflows and APIs8 minutes

2 assignmentsTotal 20 minutes

HOL: Build a Simple Multi-Step Agent Using LangChain or Rasa10 minutes
HOL: Intelligent Agent Lab10 minutes

In the final lesson, learners will focus on evaluating how agents perform in realistic, changing environments. They'll explore testing strategies, interpret edge-case behaviors, and fine-tune agents using logs, performance feedback, and outcome tracking. Examples such as AlphaCode’s reasoning iterations and BabyAGI’s task queue refinement will help frame the concepts. This lesson culminates in the Capstone project, where learners will apply everything they've learned to design and deliver an intelligent, goal-driven agent.

What's included

4 videos1 reading4 assignments

4 videosTotal 19 minutes

Is Your Agent Really Working? 7 minutes
Agent Behavior Breakdown: Debugging and Testing5 minutes
BabyAGI, AlphaCode: Improving Agent Performance Over Time5 minutes
Congratulations and Continuous Learning Journey3 minutes

1 readingTotal 6 minutes

Edge Cases, Loops, and Failure Modes in Agent Systems6 minutes

4 assignmentsTotal 110 minutes

Assessment30 minutes
HOL: Diagnose and Improve an Agent’s Behavior Using Logs and Examples10 minutes
HOL: Diagnose and Improve an Agent’s Behavior Using Logs and Edge Cases10 minutes
Project: Design and Deploy a Real-World AI Agent60 minutes