A — Introduction, objectives and prerequisites | Agentic AI — Chapter 1: Introduction and Foundations
Detailed Chapter 1 of the Agentic AI learning track: all 15 foundation topics, architecture diagrams, runnable Python, Java and React examples, solved questions, 15 exercises, a complete mini-project, interview preparation and revision. Chapters 2–20 are upcoming.
Prerequisites: Beginner-friendly theory. Basic familiarity with functions helps for the Python lab; the runnable example uses only the standard library. No provider credentials are required. This is Chapter 1 of a planned 20-chapter track, not a completion claim for the entire course.
Section A — Chapter Introduction
An agentic system does more than produce a reply: it uses observations to decide what permitted step to take next. This chapter builds the vocabulary and engineering model needed to understand that loop. We start with ordinary AI, machine learning and generation, then connect them to goals, tools, state and evaluation. The central example is a read-only job-search assistant. You will study a complete bounded controller using synthetic data, inspect failure cases and design a small project without needing a paid model account.
By the end, you should be able to explain which parts of a system are learned, generated or deterministic; distinguish an interface from a control architecture; trace an agent run; and identify where policy must be enforced. The goal is not to maximize autonomy. It is to choose an appropriate amount of flexibility and demonstrate that the system meets a concrete objective within its permissions and resource limits. Later chapters develop Python, frameworks, memory, MCP and production deployment in depth.
Learning objectives
Define AI, machine learning, deep learning, generative AI and agentic behavior without treating them as synonyms.
Compare a basic chatbot, deterministic workflow and bounded agent using the same job-search task.
Identify goals, tools, state, observations, controllers and evaluators in an architecture.
Trace normal, denied, cancelled and budget-exhausted runs.
Implement and test a small read-only controller with an explicit mock decision component.
Explain authorization, approval, evidence support and responsible data use as separate responsibilities.
Prerequisites and study approach
No machine-learning mathematics or framework experience is required for the theory. Basic familiarity with files, web applications and functions is helpful. The Python example uses only the standard library and includes an explanation of its structures; readers new to programming can follow the run trace first and revisit the implementation after Chapter 2. Java and React snippets illustrate application boundaries rather than a complete Spring Boot deployment. Allow roughly 8–10 hours for reading, exercises and revision, with additional time if programming is new.
Industry applications
A useful first application has accessible evidence, clear permissions and reversible outputs. Examples include suggesting relevant vacancies, drafting support answers from approved policies and preparing a research summary with source IDs. Use these as design exercises, not claims of universal reliability. The same architecture must be adapted to each actual data source and affected user. In this chapter all job records are synthetic and every tool is read-only; no application is sent and no provider API is invoked.
Chapter 1 — Introduction to Agentic AI