Agentic AI: Building the Next Generation of Smart Agents

Name: Agentic AI: Building the Next Generation of Smart Agents
Rating: 3.8 (66 reviews)

Master Agentic AI — create smart, self-directed agents powered by LLMs, memory, and orchestration frameworks.

Created byData Science Academy, School of AI

Last updated 10/2025

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What you'll learn

Understand the Foundations of Agentic AI Learners will grasp the core principles of agency in AI, differentiating between chatbots.
Build and Deploy Intelligent AI Agents Students will gain hands-on experience designing, coding, and orchestrating AI agents that can perceive, plan.
Integrate Memory, Knowledge Retrieval, and Workflow Automation Learners will develop agents capable of retaining context, retrieving information.
Design Safe, Aligned, and Ethical AI Systems Students will learn to implement safety mechanisms, ethical guardrails, and alignment strategies.

Course content

4 sections • 17 lectures • 3h 35m total length

1.1 What is Agentic AI?12:08
Agentic AI delivers autonomous agents that perceive, plan, act, and reflect to achieve outcomes with autonomy, reasoning, adaptability, and proactivity. It enables scalable automation using tools and memory across industries.
1.2 Why Agentic AI Matters Now13:25
Explore why agentic AI matters now as autonomous, reasoning agents manage end-to-end tasks in real time, enabled by large language models, tools, and memory pipelines.
1.3 Core Concepts of Agency in AI26:59
Explore how agentic AI turns perception, planning, action, reflection, and memory into autonomous, goal-driven systems that collaborate with humans, stay aligned with ethics, and learn from outcomes.
Hands on Lab1:20

2.1 Agents vs Chatbots vs Assistants26:10
Trace the evolution from rule-based chatbots to context-aware assistants and autonomous AI agents, highlighting perception, planning, action, and reflection in a cognitive loop with external tool use.
2.2 Key Components of Agents15:03
Explore the anatomy of agentic ai systems: perception, reasoning, memory, planning, and action, and learn how modular layers, feedback loops, and safety controls enable adaptive, autonomous agents.
2.3 Architectures for Agentic AI15:38
Explore agentic AI architectures that fuse perception, reasoning, memory, and action into modular, self-evolving systems with reflective loops for autonomous, adaptive intelligence.
Hands on Lab1:35

3.1 Large Language Models (LLMs) as Agents14:41
Power agentic ai by using large language models as cognitive engines, adopting transformer architecture, memory, and external tool integrations to plan, reason, and act autonomously in multi-agent systems.
3.2 Memory & Knowledge Retrieval14:11
Explore how agentic ai uses multi-layer memory—short term memory, working memory, and long term memory—plus retrieval augmented generation to enable adaptive reasoning and continuous learning.
3.3 Tools & APIs14:27
Explore how tools and APIs connect reasoning to action, enabling agents to fetch live data, trigger workflows, and reason, act, and learn across data, decisions, and domains.
3.4 Orchestration & Workflow Engines14:14
Explore authentic AI orchestration that coordinates multi-agent tools, memory, and execution through a central orchestrator, enabling autonomous, resilient workflows with robust monitoring and self-improvement.
Hands on Lab1:33

4.1 Multi-Agent Systems13:57
Explore how multi-agent systems enable collaborative, autonomous agents to solve complex problems through a shared environment, data exchange, and learning across specialized roles and intelligent coordination.
4.2 Agent Autonomy Levels13:56
Trace the spectrum of agent autonomy from manual to fully autonomous agents, covering levels zero to five and the four dimensions—decision making, learning, goal formulation, and human oversight.
4.3 Safety, Alignment & Guardrails14:24
Explore how alignment, guardrails, and oversight create safe, trustworthy agentic AI guided by transparency, accountability, and reliability, while value alignment techniques and global governance shape responsible autonomy.
Hands on Lab1:42

Requirements

Basic Programming Knowledge (Recommended) Familiarity with Python will help you understand and implement hands-on labs more easily.
Interest in AI, Automation, or Machine Learning Curiosity about how intelligent systems think, reason, and act is more important than deep technical expertise.
Comfort Using Online Tools or Jupyter Notebooks Labs will use browser-based environments such as Google Colab or Jupyter for coding demonstrations.
Internet Connection and a Computer Needed to access course content, run AI demos, and explore APIs or frameworks introduced in the lessons.

Description

“This course contains the use of artificial intelligence”

Agentic AI – Building the Next Generation of Smart Agents

Step into the future of Artificial Intelligence with this comprehensive course on Agentic AI — the evolution of systems that don’t just react but reason, plan, and act autonomously.

In this course, you’ll learn how to create intelligent AI agents that combine decision-making, memory, and tool integration to perform tasks independently and safely. You’ll explore how Large Language Models (LLMs), retrieval mechanisms, and workflow orchestration come together to build powerful agentic systems capable of solving real-world problems.

Through hands-on labs and practical exercises, you will:

Build a goal-driven autonomous agent using Python
Implement memory and knowledge retrieval for context-aware reasoning
Integrate tools and APIs for real-time actions
Design multi-agent systems that collaborate to complete complex tasks
Add ethical guardrails and safety layers to ensure responsible autonomy

Each section includes step-by-step coding labs in Jupyter or Google Colab, helping you gain practical skills from theory to implementation.

By the end of this course, you’ll have mastered the foundational principles and technical workflows to design, deploy, and manage Agentic AI systems that are intelligent, explainable, and aligned with human values.

No advanced AI knowledge required — just passion, curiosity, and a desire to shape the future of autonomous intelligence.

Who this course is for:

AI Enthusiasts and Developers Curious about how large language models (LLMs) evolve into full-fledged agents capable of reasoning, planning, and acting autonomously.
Data Scientists, ML Engineers, and Software Developers Looking to extend your existing AI or automation skills into the emerging field of Agentic AI — integrating memory, tools, APIs, and workflows.
Business Analysts, Product Managers, and Innovators Interested in applying intelligent agents to real-world use cases such as customer support, workflow automation, or AI-driven decision-making.
Students and Researchers Seeking a practical, hands-on introduction to the rapidly growing domain of autonomous AI systems and multi-agent collaboration.
Anyone Curious About the Future of AI Whether technical or non-technical, this course helps you understand how AI systems can safely act, adapt, and align with human goals.

Agentic AI: Building the Next Generation of Smart Agents

What you'll learn

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Course content

Section 1: Introduction to Agentic AI4 lectures • 54min

Section 2: Foundations of AI Agents4 lectures • 58min

Section 3: Building Blocks & Tools5 lectures • 59min

Section 4: Designing Agentic Workflows4 lectures • 44min

Requirements

Description

Who this course is for: