inaraX

Agentic AI

For anyone building or overseeing AI agents

Agentic AI covers what separates an agent from a chatbot: the loop of reasoning, acting, and observing, the tools an agent reaches for, and the orchestration that turns one agent into a working system. It ends where production begins — reliability, evaluation, security, and governance.

Created by InaraX
Instruction language: English
Premium

Build multi-agent systems · APIs, agents, and deployment · End-to-end implementation practice

Highlights for this program

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Earn a certificate from InaraX

After completion of all courses in the InaraX Agentic AI Professional Certificate path, earn a professional certificate that you can share on social media, LinkedIn, resume, or CV.

What you'll learn

  • ✓What an agent is, and what it is not
  • ✓The reason–act–observe loop, and where agents get stuck in it
  • ✓Tools, memory, and how an agent chooses its next move
  • ✓Building a working agent end to end
  • ✓Orchestrating agents into reliable workflows
  • ✓Multi-agent systems and the coordination problems they create
  • ✓Evaluating agent behaviour beyond 'it looked right'
  • ✓Security and governance for agents acting on real systems

Course content

3 levels • 6 modules

  • Module 1: What Is an Agent?Locked
  • Module 2: How Agents Think and ActLocked

Requirements

  • Comfort using AI chat tools day to day.
  • Helpful (not required): having written a prompt you were pleased with, and one that failed.

Description

Agentic AI is a level-based program (Associate → Intermediate → Advanced) for anyone who has used AI tools and now wants to understand the systems being built on top of them. You'll start with what an agent actually is and how one decides what to do next, then build a working agent and orchestrate it into a workflow, and finish with multi-agent systems and the practices that keep them reliable, secure, and accountable.

By the end of this course, you will:

  • Explain what makes something an agent rather than a chatbot.
  • Describe the reason–act–observe loop and where each step goes wrong.
  • Build a working agent and give it the right tools for a task.
  • Orchestrate agents into workflows, and know when a workflow is the wrong shape.
  • Design multi-agent systems and understand how they fail.
  • Evaluate, secure, and govern agents well enough to put them in front of real work.

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