Agentic AI Course: Build AI Agents, Workflows and Autonomous Systems
Learn how to design, build, and operate AI agent systems that receive goals, plan tasks, use tools, execute actions, manage state, and complete real-world workflows. Master agent architecture, tool execution, API orchestration, multi-agent systems, error handling, security guardrails, and agent observability. Build practical agentic AI projects with Python.
What's Inside Every Lesson?
Every topic is designed so complete beginners can follow along step by step
Interactive Code Editor
Write & run real code in the browser - no setup needed
Practice After Every Topic
Hands-on exercises reinforce what you just learned
Q&A Flip Cards
Common questions answered with simple explanations
Module Quizzes
Test your knowledge before moving to the next module
Mini Projects
Build real apps - a calculator, to-do list, and more
Visual Diagrams
Concepts explained with clear, annotated diagrams
Who This Course Is For
Designed for developers, students, and working professionals who want to build AI systems that take actions, not just generate text. Ideal for anyone who wants to move from using AI tools to engineering AI agent workflows.
Prerequisites
Basic programming knowledge is helpful but not mandatory. Familiarity with Python is useful for the code sections. No prior knowledge of AI agents or Generative AI is required.
First published November 2024 · Updated 2026
What You'll Learn
- What an AI agent is and how it differs from a chatbot or traditional automation
- The core architecture of an agent: controller, state, tools, planner, and guardrails
- How to build and register tools that agents can call to perform real-world actions
- How to orchestrate multiple external APIs inside a single agent workflow
- How agents store and manage state: temporary, persistent, conversation, and task state
- How to break a complex goal into a sequence of planned subtasks
- How to design sequential, conditional, and parallel agent workflows
- How to build multi-agent systems with coordinator, worker, and reviewer roles
- How to make agents reliable with retry logic, fallback actions, and human escalation
- How to implement security guardrails, least-privilege permissions, and audit logging
- How to test, trace, and monitor agent behavior in production
- How to build four practical agentic AI projects from scratch
Career Opportunities
Course Modules Overview
Introduction to Agentic AI
15 topics
AI Agent Architecture
16 topics
Tools and Function Execution
15 topics
API Orchestration for AI Agents
15 topics
Agent Memory and State Management
16 topics
Agent Planning and Task Decomposition
16 topics
AI Workflow Orchestration
16 topics
Multi-Agent Systems
16 topics
Building Reliable AI Agents
16 topics
AI Agent Security and Guardrails
16 topics
Agent Testing and Observability
17 topics
Building Real-World Agentic AI Systems
17 topics
Complete all 12 modules to unlock your course completion certificate
Course Curriculum
12 comprehensive modules covering everything from basics to advanced topics
Introduction to Agentic AI
Understand what Agentic AI is and how it differs from chatbots and traditional automation. Learn what an AI agent is, how it receives goals, makes decisions, and takes actions in the real world.
AI Agent Architecture
Understand the internal components of an AI agent system. Learn about the controller, state, goals, tools, observations, and how each component works together in the agent execution cycle.
Tools and Function Execution
Learn how AI agents use tools to perform actions beyond text generation. Build practical tools for calculation, date lookup, file reading, and database queries. Learn to validate inputs and handle tool errors safely.
API Orchestration for AI Agents
Learn how AI agents orchestrate external APIs to perform real-world actions. Understand request and response handling, authentication, error recovery, rate limits, and how to connect multiple APIs in a single agent workflow.
Agent Memory and State Management
Understand how AI agents store, read, and update information during task execution. Learn the difference between temporary, persistent, and conversation state. Design safe memory systems that handle failures and data expiry.
Agent Planning and Task Decomposition
Learn how AI agents break large goals into manageable steps. Understand sequential, conditional, and dynamic planning. Build a planning workflow that decomposes a business goal, validates each step, and handles failures with replanning.
AI Workflow Orchestration
Understand how to build structured AI workflows that connect agents, tools, and human approval steps. Learn sequential, conditional, and parallel workflows. Add timeouts, recovery, and logging to make workflows production-ready.
Multi-Agent Systems
Learn how to build systems where multiple specialized agents work together. Understand coordinator, worker, and reviewer roles. Design sequential and parallel multi-agent workflows, handle handoffs, and manage shared state across agents.
Building Reliable AI Agents
Learn how to build AI agents that recover from failures rather than crashing. Understand common failure modes including tool errors, API failures, bad arguments, and infinite loops. Implement retry logic, fallback actions, validation, and human escalation.
AI Agent Security and Guardrails
Understand the security risks specific to AI agent systems. Learn how to implement least-privilege permissions, human approval gates, action restrictions, secret management, and audit logging. Design guardrails that prevent unsafe agent behavior.
Agent Testing and Observability
Learn how to test and observe AI agent systems in development and production. Understand why agent testing differs from standard software testing. Implement execution traces, cost tracking, success rate monitoring, and regression testing for agent workflows.
Building Real-World Agentic AI Systems
Apply everything from the course to design and build complete real-world agentic AI systems. Create a Personal Task Agent, Customer Support Agent, and Multi-Agent Business Research System. Learn production deployment, cost control, and evaluation.
Your Learning Roadmap
Follow this structured path - from first concepts to production-ready mastery
Understand what AI agents are, how they differ from chatbots, and learn the core agent architecture with goals, tools, and decision loops
Master tool execution, API orchestration, state management, planning, workflow design, multi-agent systems, and reliability engineering
Build production-grade agentic systems with security guardrails, observability, testing pipelines, cost controls, and real-world deployment
Understand what AI agents are, how they differ from chatbots, and learn the core agent architecture with goals, tools, and decision loops
Master tool execution, API orchestration, state management, planning, workflow design, multi-agent systems, and reliability engineering
Build production-grade agentic systems with security guardrails, observability, testing pipelines, cost controls, and real-world deployment
Tools & Technologies
Essential tools you'll master during this course
Python
Primary language for building agent systems, tools, and workflows
LangGraph
Graph-based framework for building stateful multi-agent workflows
OpenAI API
API access to GPT models used as the reasoning engine inside agents
CrewAI
Framework for orchestrating role-based multi-agent teams
REST APIs
HTTP APIs that agents call to perform real-world actions
JSON
Standard format for tool inputs, outputs, and agent state storage
Ready to Start Learning?
Begin your journey with Module 1 and build your skills step by step. Completely free, no registration required.
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