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Agentic AI for Absolute Beginners - A 52 Week Course
Published 9/2026
Created by School of AI, Arjun Vaid
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 364 Lectures ( 48h 55m ) | Size: 33.1 GB
Learn AI agents, tools, memory, workflows, safety, automation, and Python through a beginner-friendly 52-week journey.
What you'll learn
Explain what agentic AI is and how AI agents differ from chatbots, traditional automation, and fixed workflows. Understand the core components of an AI agent, including goals, tasks, actions, decisions, observations, and feedback loops. Break complex goals into smaller tasks and organize them using plans, checklists, dependencies, and decision trees. Write clearer prompts using instructions, context, constraints, examples, and structured output formats. Generate consistent AI outputs using bullet lists, tables, templates, and JSON structures. Understand how agents use tools such as web search, calculators, APIs, files, and external applications. Apply source verification and safe browsing practices when agents gather current information. Explain short-term context, long-term memory, state management, and common memory limitations. Design planning, reflection, correction, retry, and error-recovery processes for agent workflows. Create human-in-the-loop systems with approval steps, review checkpoints, escalation, and override controls. Use beginner-level Python concepts, including variables, functions, loops, conditionals, lists, and dictionaries. Understand APIs, requests, responses, API keys, JSON, model settings, token limits, and cost considerations. Design simple research, writing, data, support, email, scheduling, meeting, and knowledge assistants. Compare agentic systems with deterministic workflows and choose the most appropriate approach. Understand retrieval, chunking, contextual grounding, citations, and hallucination-reduction techniques. Evaluate agent outputs using success criteria, test cases, edge cases, scoring, and regression testing. Debug failures involving prompts, tools, memory, planning, context, and workflow logic. Apply guardrails, privacy controls, restricted actions, safe fallbacks, and responsible AI principles. Design multi-step and introductory multi-agent systems using specialized roles and handoff logic. Use logging, tracing, observability, deployment, access control, versioning, and maintenance practices. Build beginner-friendly automations using both Python-based and no-code tools. Complete three mini-projects and a portfolio-ready capstone agent with documentation, testing, and a final demonstration.Requirements
No previous experience with agentic AI, artificial intelligence, programming, or automation is required. The course is designed specifically for absolute beginners. Basic computer, file-management, web-browsing, and communication skills are sufficient. A computer with a reliable internet connection is recommended. Access to a generative AI assistant will support the practical exercises. Python will be introduced gradually, so no previous coding knowledge is necessary. A code editor or beginner-friendly notebook environment will be useful during the Python sections. Some demonstrations may use APIs, but the fundamental concepts can be understood without paid tools. Students may use no-code platforms for selected workflow and automation exercises. No advanced mathematics, machine learning, or data science knowledge is required. Learners should be willing to experiment with prompts, test workflows, review outputs, and troubleshoot mistakes. Consistency and curiosity are more important than technical experience. Students should be prepared to complete weekly reviews, mini-projects, and a final capstone.Description
This course contains the use of artificial intelligence.
Agentic AI for Absolute Beginners - A 52-Week Course is a complete, beginner-friendly learning journey designed to help you understand, design, test, and demonstrate practicalAI agents. No previous programming, automation, machine learning, or artificial intelligence experience is required.
You will begin by learningwhat agentic AI is and how agents differ from chatbots and traditional workflows. You will explore goals, tasks, actions, decisions, observations, feedback loops, planning, iteration, and human oversight. These concepts will help you understand how agents move beyond answering questions and begin completing structured tasks.
The course introducesprompt engineering for AI agents, including clear instructions, context, constraints, examples, output formatting, tables, checklists, and JSON. You will learn how to break complex goals into manageable steps and create reliable workflows that are easier to test and improve.
You will then exploreAI tool use, including web search, calculators, APIs, files, and external actions. You will learn how agents gather information, verify sources, call tools, inspect results, and decide what to do next. Memory, context management, planning, reflection, correction, and retry logic are explained using approachable examples.
A gradual introduction toPython for AI agents covers variables, data types, functions, loops, conditionals, lists, dictionaries, scripts, APIs, requests, responses, and model integration. You will use these foundations to understand how simple assistants and agent workflows are created.
Practical modules introduce research helpers, writing assistants, data helpers, support agents, email assistants, scheduling assistants, meeting assistants, and internal knowledge assistants. You will also compareAI agents vs automation workflows so you can select the right design for each problem.
The course coversretrieval-augmented generation, document grounding, chunking, contextual prompts, citations, and hallucination reduction. You will learn how agents can work with files and trusted knowledge rather than relying only on a model's existing knowledge.
Reliability and safety are central throughout the program. You will studyagent evaluation, debugging, guardrails, privacy, restricted actions, human approvals, responsible AI, logging, tracing, observability, cost management, deployment, and maintenance.
You will also explore introductorymulti-agent systems, including planner, researcher, writer, reviewer, executor, and coordinator roles. You will learn when specialized agents add value and when a simpler workflow is more reliable.
During the final eight weeks, you will complete mini-projects and build a portfolio-ready capstone. You will define the problem, choose tools, design the workflow, add guardrails, test reliability, improve the user experience, document the system, and present a final demonstration.
By the end of this52-week agentic AI course, you will have a practical foundation in AI agents, prompting, tools, memory, workflows, Python, APIs, RAG, automation, evaluation, safety, and deployment.
Who this course is for
Complete beginners who want a structured introduction to agentic AI. Professionals who want to understand how AI agents can support everyday work. Students and recent graduates interested in modern AI and automation careers. Career changers exploring roles involving generative AI, intelligent workflows, or AI operations. Entrepreneurs and small-business owners seeking practical automation opportunities. Business analysts, consultants, and project managers evaluating agentic AI use cases. Content creators, educators, and researchers interested in AI-powered assistants. Customer-support, operations, marketing, sales, and administrative professionals. Developers who are new to AI agents and want to understand the foundational patterns. No-code users who want to progress from simple automation to more adaptive AI workflows. Team leaders responsible for reviewing, approving, or governing AI-assisted processes. Anyone who wants to build practical AI agents without beginning with advanced mathematics or complex software engineering.Homepage
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