Artificial Intelligence AI Agents

AI Agents: Concepts, Capabilities, and Practical Use

A structured introduction to agentic AI systems, from core concepts to reliable real-world workflows

Log In to Enroll About this course Get unlimited access
AI Agents: Concepts, Capabilities, and Practical Use logo
Quick Course Facts
18
Self-paced, Online, Lessons
18
Videos and/or Narrated Presentations
6.3
Approximate Hours of Course Media
About the AI Agents: Concepts, Capabilities, and Practical Use Course

AI Agents: Concepts, Capabilities, and Practical Use is a practical online course for understanding how Artificial Intelligence agents work and how to apply them in real workflows. Students will learn the foundations, architecture, design patterns, and reliability practices needed to move from simple prompts to useful agentic systems.

Build Reliable AI Agent Workflows For Practical Use

  • Learn a structured introduction to agentic AI systems, from core concepts to reliable real-world workflows
  • Understand how agents use goals, context, tools, memory, retrieval, and planning to complete tasks
  • Explore practical design patterns for single-agent workflows, multi-agent collaboration, and business system integration
  • Develop safer deployment habits through testing, monitoring, human review, security, privacy, and governance practices

This course explains how Artificial Intelligence agents are designed, evaluated, connected to tools, and deployed responsibly.

Through focused lessons, students begin with the foundations of AI agents, including what makes an AI system an agent, how agentic workflows differ from traditional chatbots, and which capabilities modern language models bring to practical automation. The course then moves into agent design basics, showing how goals, instructions, context, constraints, and prompting patterns shape agent behavior.

Students will study the architecture behind useful agent systems, including tool use, function calling, planning loops, task decomposition, memory, retrieval-augmented agents, and knowledge access. The course also covers how to build practical agents that connect to APIs, files, and business systems, with clear attention to single-agent workflows and multi-agent collaboration patterns.

AI Agents: Concepts, Capabilities, and Practical Use also emphasizes reliability and operational control. Students learn how to add human-in-the-loop review, monitor agent activity, evaluate performance, reduce common failure modes, and account for security, privacy, and governance concerns. By the end of the course, students will be able to think clearly about Artificial Intelligence agent design and create more reliable, responsible workflows from concept to practical use.

Course Lessons

Full lesson breakdown

Lessons are organized by topic area and each includes descriptive copy for search visibility and student clarity.

Foundations of AI Agents

3 lessons

Lesson 1: What Makes an AI System an Agent

18 min Preview
This lesson defines what makes an AI system an agent rather than a simple chatbot, script, or prediction model. It introduces the core ingredients of agentic behavior: goals, perception of context, re…

Lesson 2: From Chatbots to Agentic Workflows

17 min
This lesson explains the shift from traditional chatbots to agentic workflows. Learners will distinguish a conversational interface from a system that can plan, use tools, track progress, and complete…

Lesson 3: Core Capabilities of Modern Language Models

20 min
This lesson explains the core capabilities that make modern language models useful as the reasoning and communication layer inside AI agents. Students will learn how models interpret instructions, gen…

Agent Design Basics

2 lessons

Lesson 4: Goals, Context, Instructions, and Constraints

18 min
This lesson explains the four design inputs that shape an effective AI agent: goals , context , instructions , and constraints . Students learn how these elements work together to turn a general-purpo…

Lesson 5: Prompting Patterns for Agent Behavior

19 min
This lesson explains how prompting patterns shape the behavior of AI agents. Learners will see how role, goal, context, constraints, tool-use instructions, planning expectations, memory boundaries, an…

Agent Architecture

4 lessons

Lesson 6: Tool Use and Function Calling

22 min
This lesson explains how AI agents use tools and function calling to move beyond text generation into practical action. Learners will see how a model decides when to call a tool, how arguments are str…

Lesson 7: Planning, Reasoning Loops, and Task Decomposition

23 min
This lesson explains how AI agents turn broad goals into workable plans, how reasoning loops guide action, and how task decomposition reduces complexity. It focuses on the architecture-level patterns …

Lesson 8: Short-Term and Long-Term Memory

20 min
This lesson explains how AI agents use memory to stay coherent within a task and become more useful across repeated interactions. It distinguishes short-term memory, which usually lives in the active …

Lesson 9: Retrieval-Augmented Agents and Knowledge Access

22 min
This lesson explains how retrieval-augmented agents access external knowledge instead of relying only on model memory. Learners examine how an agent can search documents, databases, APIs, and internal…

Building Practical Agents

3 lessons

Lesson 10: Single-Agent Workflow Design

21 min
This lesson explains how to design a useful single-agent workflow: one AI agent with a clear goal, defined inputs, tool access, decision rules, checkpoints, and an output format. It focuses on practic…

Lesson 11: Multi-Agent Collaboration Patterns

22 min
Multi-agent systems use more than one agent-like component to solve work that is too broad, uncertain, or specialized for a single agent to handle cleanly. In this lesson, Professor Amit Kumar explain…

Lesson 12: Connecting Agents to APIs, Files, and Business Systems

23 min
This lesson explains how practical AI agents connect to the outside world through APIs, files, databases, and business systems. Learners will see how tools turn an agent from a text-only assistant int…

Operational Control

2 lessons

Lesson 13: Human-in-the-Loop Review and Approval Flows

19 min
This lesson explains how human-in-the-loop review and approval flows keep AI agents useful without giving them unchecked authority. Learners will see where human judgment should be inserted, how appro…

Lesson 14: Monitoring, Logging, and Observability for Agents

21 min
This lesson explains how operational teams monitor, log, and observe AI agents after they move beyond simple demos. Students learn what must be captured from agent runs, why traditional application lo…

Reliability and Quality

2 lessons

Lesson 15: Testing and Evaluating Agent Performance

23 min
This lesson explains how to test and evaluate AI agent performance in practical workflows. Students learn why agent evaluation is different from ordinary software testing, how to define success criter…

Lesson 16: Common Failure Modes and How to Reduce Them

22 min
This lesson examines the most common ways AI agents fail in practical workflows and how teams can reduce those failures before they reach users or business processes. It focuses on reliability issues …

Responsible Deployment

1 lesson

Lesson 17: Security, Privacy, and Governance Considerations

24 min
This lesson examines the security, privacy, and governance issues that become more important when AI systems can plan, use tools, access data, and take actions on behalf of users. The focus is not abs…

Capstone Application

1 lesson

Lesson 18: Designing a Practical Agent Use Case from End to End

25 min
In this capstone lesson, learners design a practical AI agent use case from end to end. The lesson focuses on translating a real workflow into a scoped agent design with clear goals, boundaries, tools…
About Your Instructor
Professor Amit Kumar

Professor Amit Kumar

Professor Amit Kumar guides this AI-built Virversity course with a clear, practical teaching style.