Agentic AI Engineer: AI Agents, ReAct, RAG & LLMs in C++

This course takes a different path.

Agentic AI Engineer: AI Agents, ReAct, RAG & LLMs in C++ is designed for developers and engineers who want to understand agentic AI systems from the inside. So, you will learn how their core building blocks can be designed, implemented, connected, tested and improved using modern C++.

This course now also includes cloud labs using AWS CloudShell and Azure Cloud Shell. These labs help you run C++ agent experiments directly in cloud terminal environments without spending too much time on local setup. The first cloud labs are added for the Agent Fundamentals experiments and more labs will continue to be added across the course.

The goal of this course is not only to explain what AI agents are. The goal is to help you understand how agentic systems are structured as software systems.

You will explore how agents receive goals, plan next steps, reason through intermediate states, call tools, observe results, retrieve relevant knowledge, use memory, coordinate with other agents and move toward safer and more production-ready architecture.

This course is especially focused on the engineering mindset behind AI agents and agentic AI.

You will learn concepts such as:

• AWS CloudShell and Azure Cloud Shell based C++ labs

• Agent loops

• PRAO-style agent reasoning

• Tool calling

• Structured tool execution

• ReAct reasoning patterns

• Retrieval-Augmented Generation

• Chunking and retrieval logic

• Context assembly

• LLM-oriented workflows

• Memory-aware agents

• Working memory and long-term memory concepts

• Episodic recall

• Multi-agent coordination

• Agent orchestration patterns

• Evaluation and test harness thinking

• Safety and control layers

• Human-in-the-loop workflows

• Logging, retries, observability, and production-minded design

This course uses self-contained C++ examples to make the architecture visible. Instead of simply calling a framework and accepting the result, you will see how the pieces fit together. This makes the learning process deeper, because every important concept becomes concrete.

Well, a reliable agentic system needs structure, boundaries, a way to decide which tool to use, a way to manage context, a way to retrieve knowledge without overwhelming the model, memory that improves usefulness without creating unsafe or unreliable behavior and it also needs evaluation, monitoring, fallback paths, and a clear way to observe what the system is doing.

This course is built around those ideas. So you will begin with the fundamentals of agentic behavior and gradually move toward more advanced architecture. You will see how an agent can move from a simple goal-driven loop into more capable patterns such as PRAO, ReAct, RAG, memory-enabled workflows and multi-agent coordination.

You will also learn why production AI systems require more than a working demo. A demo can look impressive once. A real system must be understandable, controllable, testable, observable and safe enough to improve over time.

By the end of the course, you will have a strong foundation for understanding and designing AI agents that are:

• More understandable

• More controllable

• More modular

• More testable

• More scalable

• Safer to extend

• Closer to real-world software architecture

This course is ideal for ones who want more than prompting. It is for developers who want to understand the engineering structure behind AI agents and agentic AI systems. If you want to stand out as an AI engineer by combining modern AI concepts with C++ system design, this course will give you a rare and valuable foundation.

What Will You Learn in This Course?

After completing this course, you will be able to:

• Understand what AI agents are and why agentic systems are becoming important in modern software.

• Build the core agent loop in C++ and understand how goal-driven AI systems operate.

• Understand the relationship between goals, actions, observations, state, and decision-making inside an agent system.

• Implement PRAO style agent behavior and use it to structure basic agent reasoning workflows.

• Run hands-on C++ agent experiments in AWS CloudShell and Azure Cloud Shell.

• Use cloud terminal environments to experiment with agent fundamentals without depending only on local setup.

• Design tool-using agents and understand how tools can be registered, selected, executed, and observed.

• Structure safer tool execution flows instead of treating tool calling as an uncontrolled black box.

• Implement the ReAct pattern so an agent can reason through Thought, Action and Observation steps.

• Build Retrieval-Augmented Generation workflows in C++ using chunking, retrieval logic, and context assembly.

• Understand how retrieved knowledge can be prepared, ranked, selected, and inserted into an agent’s working context.

• Create memory-aware agents using working memory, long-term memory concepts, and episodic recall patterns.

• Design multi-agent workflows using orchestration patterns such as pipeline, dispatch, debate, and coordinator-worker structures.

• Evaluate agent quality using reliability, latency, safety, output consistency and test harness design.

• Understand why AI agent systems need logging, retries, fallback logic, traces, human approval points, and observability.

• Learn how agentic systems evolve from educational examples toward production-ready software architecture.

• Develop a stronger engineering mindset for building AI systems beyond prompts, demos and high-level frameworks.

Why This Course Is Different

Many AI agent courses focus on how to use a specific framework. Frameworks are useful, but they can hide the most important engineering decisions. If you only learn the framework, you may know how to run a demo, but you may not understand why the system behaves the way it does.

In this course, you will study the underlying architecture:

• How the agent loop is organized

• How reasoning and action steps are separated

• How tools are represented

• How retrieval is connected to context

• How memory changes the behavior of an agent

• How multi-agent coordination can be structured

• How safety and human control can be added

• How observability helps you debug agent behavior

• How production AI systems require reliability, not only impressive outputs

• How cloud-based labs can make experiments easier to run and repeat

The course uses C++ because C++ forces the system design to be explicit. You will not hide everything behind a high-level abstraction. You will see the data structures, control flow, runtime decisions, and architecture patterns more clearly. That makes this course valuable not only for C++ developers, but also for AI engineers who want to understand agentic systems at a deeper level.

The AWS CloudShell and Azure Cloud Shell labs add another practical layer. They help you run experiments in real cloud terminal environments, compare behavior, and focus on the agent design instead of spending too much time on local configuration.

What Are the Prerequisites?

You do not need:

• Prior experience with AI agents

• Prior experience with agentic AI

• Advanced mathematics

• Deep learning background

• Experience with large AI frameworks

• Prior knowledge of ReAct, RAG, memory systems or multi-agent architecture

• AWS or Azure experience

This course may not be ideal for:

• Absolute beginners with no programming background at all.

• Ones looking only for no-code AI tools.

• Ones who only want prompt templates without understanding the system design behind them.

• People looking for a course focused only on a single AI agent framework.

• Ones who want a pure theory course without implementation-oriented thinking.

• Developers who do not want to read or write C++ code.