AI Agents Course — Build Autonomous LLM Agents | Practical Guide & Hands-On Labs

AI Agents: A Practical Guide to Building Autonomous Systems

Practical, hands-on training to build, test, and deploy autonomous LLM-powered agents and multi-agent systems for real-world automation.

Yucaerin Yucaerun

5.0

English

Certificate Course

99+ Students

10h++

Course Overview

Learn how to design, build, and deploy autonomous AI agents that perceive environments, plan, act, and integrate with real-world tools and APIs. This hands-on course covers agent architectures, prompt & planning strategies, memory and state management, multi-agent coordination, safety & guardrails, and practical deployments using modern frameworks (LangChain, AutoGen, Hugging Face tools).

FAQ

An AI agent is an autonomous system that perceives inputs, reasons/ plans, and takes actions (often via tools/APIs) to achieve goals — not just respond to single-turn prompts. Agents often include memory, planning, tool use and the ability to act in the environment.

Practical labs use modern agent frameworks and toolkits such as LangChain patterns, Microsoft AutoGen (AutoGen-style orchestration), and Hugging Face agent tooling — plus example deployments that call external APIs and services.

No — the course is practical. Basic Python and familiarity with LLM concepts (prompts, token limits) are sufficient; advanced modules cover RL/planning and production hardening for students with ML backgrounds. (See Requirements below.)

Yes. You’ll learn safety patterns (guardrails, human-in-the-loop), monitoring, and strategies for reducing token/call costs while preserving reliability.

Labs show how to run agents locally, containerize agents for production, connect to serverless endpoints and integrate with common cloud APIs — plus tips for logging, observability and scaling multi-agent systems.

Course curriculum

- Introduction

- Introduction

- Introduction

Requirements

Basic Python programming (functions, HTTP requests).
Familiarity with basic machine-learning/LLM concepts (what an LLM is, prompt basics).
A code editor (VS Code recommended), Git, and access to a terminal.
Optional but recommended: experience with REST APIs, Docker, and cloud basics (AWS/GCP/Azure).

Outcomes

Design agent architectures — choose appropriate agent types (reactive, deliberative, hierarchical) and design components for perception, memory, planner, and executor.
Build LLM-powered agents with tool integration (APIs, function calls, web scraping, databases) using LangChain / agent frameworks.
Implement memory and state (short-term and long-term) to enable persistent, context-aware agent behavior.
Create multi-agent workflows that coordinate tasks, delegate subtasks, and resolve conflicts.

Instructor

Yucaerin Yucaerun

0.0

1 Student
27 Courses
0 Review

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Last Updated
Oct 08, 2025
Students
99+
language
English
Duration
10h++
Level
beginner
Expiry period
Lifetime
Certificate
Yes