Building AI Agents for Automation

About this course

An AI agent is a system capable of independently carrying out tasks and reaching objectives by creating its own workflows and leveraging available tools, instead of simply reacting to single prompts. It operates using large language models (LLMs) as its core “brain,” allowing it to reason, plan, learn, and make informed decisions.

The AI Agents tutorial is prepared for students, engineers, and professionals. This tutorial will be useful for understanding the AI agent concepts for AI enthusiasts.

Components of AI AgentsAn AI agent generally consists of several interconnected modules that enable its advanced functionality:

• Planning Module: Decomposes complex objectives into smaller, actionable steps and organizes them in a logical order.

Memory Module: Maintains context by storing information across interactions, combining short-term memory (such as recent conversations) with long-term memory (past knowledge and experiences).

Tool Integration: Interfaces with external tools, APIs, and software to execute tasks like data retrieval, email automation, or database queries.

Learning and Reflection: Incorporates feedback loops to assess its outputs, learn from errors, and continuously enhance its performance.

Course Lessons

Section A: Introduction to AI Agents1. AI Agents – Overview and Components2. AI Agents - Architecture3. AI Agents vs Agentic AI4. Types of AI Agents5. Advantages of AI Agents6. Disadvantages of AI Agents7. AI Agents – Use Cases

Section B: MCP Live Running Example8. Build a Voice AI Agent

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