Agentic AI Engineer: AI Agents, ReAct, RAG & LLMs in C++ | Udemy [Update 06/2026]
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What you'll learn:
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[*]Use AWS CloudShell and Azure Cloud Shell to compile and run C++ AI engineering labs
[*]Build the core agent loop in C++ and understand how goal-driven AI systems operate
[*]Understand how goals, actions, observations, state and decisions connect inside an agent system
[*]Implement PRAO-style agent behavior to structure basic agent reasoning workflows
[*]Implement the ReAct pattern so an agent can reason step by step through Thought, Action, and Observation
[*]Build RAG pipelines in C++ using chunking, retrieval logic, evidence ranking, and context assembly
[*]Design and manage tool-using agents with structured execution, routing logic, and safer workflows
[*]Create memory-aware agents with working memory, long-term memory concepts and episodic recall patterns
[*]Understand how multi-agent systems coordinate through orchestration patterns such as pipeline, dispatch, and debate
[*]Evaluate agent quality using practical engineering ideas such as latency, safety, reliability, consistency, and test harness thinking
[*]Understand how agent systems move toward production-ready architecture with logging, retries, fallback paths, and clear execution traces
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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. Instead of treating agents as black boxes, 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.
You will learn concepts such as:
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[*]AWS CloudShell and Azure Cloud Shell based C++ labs
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[*]Agent loops
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[*]PRAO-style agent reasoning
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[*]Tool calling
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[*]Structured tool execution
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[*]ReAct reasoning patterns
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[*]Retrieval-Augmented Generation
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[*]Chunking and retrieval logic
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[*]Context assembly
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[*]LLM-oriented workflows
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[*]Memory-aware agents
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[*]Working memory and long-term memory concepts
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[*]Episodic recall
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[*]Multi-agent coordination
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[*]Agent orchestration patterns
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[*]Evaluation and test harness thinking
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[*]Safety and control layers
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[*]Human-in-the-loop workflows
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[*]Logging, retries, observability, and production-minded design
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The 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.
Real AI engineering is not only about sending prompts to a model. Real AI engineering is about building systems around intelligence.
A reliable agentic system needs structure. It needs boundaries. It needs a way to decide which tool to use. It needs a way to manage context. It needs a way to retrieve knowledge without overwhelming the model. It needs memory that improves usefulness without creating unsafe or unreliable behavior. 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.
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:
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[*]More understandable
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[*]More controllable
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[*]More modular
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[*]More testable
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[*]More scalable
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[*]Safer to extend
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[*]Closer to real-world software architecture
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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 real 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:
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[*]Understand what AI agents are and why agentic systems are becoming important in modern software.
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[list]
[*]Build the core agent loop in C++ and understand how goal-driven AI systems operate.
[/list]
[list]
[*]Understand the relationship between goals, actions, observations, state, and decision-making inside an agent system.
[/list]
[list]
[*]Implement PRAO style agent behavior and use it to structure basic agent reasoning workflows.
[/list]
[list]
[*]Run hands-on C++ agent experiments in AWS CloudShell and Azure Cloud Shell
[/list]
.
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[*]Use cloud terminal environments to experiment with agent fundamentals without depending only on local setup.
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[*]Design tool-using agents and understand how tools can be registered, selected, executed, and observed.
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[*]Structure safer tool execution flows instead of treating tool calling as an uncontrolled black box.
[/list]
[list]
[*]Implement the ReAct pattern so an agent can reason through Thought, Action and Observation steps.
[/list]
[list]
[*]Build Retrieval-Augmented Generation workflows in C++ using chunking, retrieval logic, and context assembly.
[/list]
[list]
[*]Understand how retrieved knowledge can be prepared, ranked, selected, and inserted into an agent's working context.
[/list]
[list]
[*]Create memory-aware agents using working memory, long-term memory concepts, and episodic recall patterns.
[/list]
[list]
[*]Design multi-agent workflows using orchestration patterns such as pipeline, dispatch, debate, and coordinator-worker structures.
[/list]
[list]
[*]Evaluate agent quality using reliability
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, latency, safety, output consistency and test harness design.
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[*]Understand why AI agent systems need logging, retries, fallback logic, traces, human approval points
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, and observability.
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[*]Learn how agentic systems evolve from educational examples toward production-ready software architecture
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.
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[*]Develop a stronger engineering mindset for building AI systems beyond prompts, demos and high-level frameworks.
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Why This Course Is Different
Many AI agent courses focus on how to use a specific framework.
This course focuses on how agent systems work.
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:
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[*]How the agent loop is organized
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[*]How reasoning and action steps are separated
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[*]How tools are represented
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[*]How retrieval is connected to context
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[*]How memory changes the behavior of an agent
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[*]How multi-agent coordination can be structured
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[*]How safety and human control can be added
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[*]How observability helps you debug agent behavior
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[*]How production AI systems require reliability, not only impressive outputs
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[*]How cloud-based labs can make experiments easier to run and repeat
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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:
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[*]Prior experience with AI agents
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[*]Prior experience with agentic AI
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[*]Advanced mathematics
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[*]Deep learning background
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[*]Experience with large AI frameworks
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[*]Prior knowledge of ReAct
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, RAG, memory systems or multi-agent architecture
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[*]AWS or Azure experience
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Friendly Note
If you already know basic C++ but are new to AI agents, this course is designed to help you enter the topic in a structured and engineering-focused way.
The course does not assume that you already understand agentic AI. Concepts are introduced step by step and connected to practical C++ examples so you can build your understanding gradually.
However, this is not a complete beginner programming course. You should already be comfortable with basic C++ code before starting.
The cloud labs are included to make experimentation easier. They are not intended to replace the core system design lessons. They are there to help you run, observe, and repeat the experiments in practical terminal environments.
Who Is This Course For?
This course is for:
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[*]C++ developers who want to enter the fast-growing field of AI agents and agentic systems.
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[*]Software engineers who want to understand how AI agents work under the hood instead of only using high-level frameworks.
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[*]AI engineers who want to strengthen their system design understanding around ReAct, RAG, tools, memory, evaluation, and production workflows.
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[*]Students and self-taught developers who want a practical and differentiated AI skill set.
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[*]Engineers building real systems who care about reliability, control, safety, observability, and production-minded design.
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[*]Developers interested in learning how agent loops, tool calling, retrieval, memory, and orchestration can be implemented from an engineering perspective.
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[*]Developers who want to go beyond prompt engineering and understand the software architecture behind intelligent systems.
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[*]Developers who want to use C++ as a way to understand AI system internals more clearly.
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[*]Learners who want hands-on cloud lab experience with AWS CloudShell and Azure Cloud Shell while studying C++ agent systems.
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[*]Anyone preparing for future work in AI applications, AI agents, intelligent software architecture, or production AI systems.
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This course may not be ideal for:
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[*]Absolute beginners with no programming background at all.
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[*]Ones looking only for no-code AI tools.
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[*]Ones who only want prompt templates without understanding the system design behind them.
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[*]People looking for a course focused only on a single AI agent framework.
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[*]Ones who want a pure theory course without implementation-oriented thinking.
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[*]Developers who do not want to read or write C++ code.
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Final Course Positioning
This course is not just about using AI agents.
It is about understanding how agentic AI systems are engineered.
You will learn how to think about agents as software systems: systems with loops, tools, state, memory, retrieval, coordination, evaluation, safety controls, cloud-based experimentation, and production constraints.
If your goal is to become stronger in AI engineering and understand what happens behind modern agentic AI systems, this course gives you a clear and practical path.
By the end, you will have a foundation that helps you move beyond surface-level AI demos and toward real agentic software architecture.

Who this course is for:
AI startup founders, entrepreneurs, and builders who want to understand the engineering foundation behind agentic AI products
Ones who want to run hands-on C++ agent labs using AWS CloudShell or Azure Cloud Shell
C++ developers who want to enter the fast-growing field of AI agents and agentic systems
AI engineers and software engineers who want to understand how agents work under the hood instead of only using high-level frameworks
Developers who want to build AI systems with a stronger foundation in agents, ReAct, RAG, LLM workflows, memory, tools, and multi-agent coordination
Students and self-taught developers who want a practical, modern, and differentiated AI engineering skill set
Engineers building real systems who care about reliability, control, safety, evaluation, and production-minded design
Developers interested in ReAct, RAG, memory, planning, tool use, and multi-agent workflows from an implementation perspective
Anyone who wants to build a stronger foundation for future work in AI applications, agent systems, intelligent software architecture, or AI product development

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