Paul Iusztin – Agentic AI Engineering (2026)

Description

Course Overview

 

Artificial intelligence is moving beyond simple chatbots and basic language-model applications toward intelligent systems that can reason through tasks, use tools, manage context, and execute multi-step workflows. Paul Iusztin – Agentic AI Engineering (2026) is a technical learning resource for developers and AI practitioners who want to understand how agentic systems are designed, implemented, evaluated, and prepared for real-world use.

Rather than focusing exclusively on prompts or isolated demonstrations, agentic AI engineering emphasizes the software architecture surrounding an AI model. This includes workflow design, tool integration, state management, evaluation, monitoring, and deployment. These engineering practices are important because an application must do more than produce an impressive response during a demonstration: it should also behave consistently, handle failures, and remain maintainable as it grows.

What Is Agentic AI Engineering?

Agentic AI engineering is the practice of developing AI-powered systems that can work through a sequence of steps to accomplish a goal. Depending on the application, an agent may gather information, call external tools, process results, maintain task context, and decide what action to take next.

For example, a research agent might collect information from several sources, organize relevant findings, and prepare a structured report. A writing workflow could coordinate research, drafting, review, and revision. Building these applications requires careful decisions about how much autonomy to give an agent, when to request human approval, and how to evaluate its results.

This course is associated with Paul Iusztin, an AI engineer and co-author of LLM Engineer’s Handbook. His production-oriented teaching approach emphasizes practical implementation and engineering decisions rather than relying only on theoretical explanations.

What Will You Learn?

1. AI Agent Foundations

Understand the principles behind agentic applications, including how language models interact with tools, how tasks are divided into steps, and how an application controls the agent’s execution. These foundations help developers distinguish a simple LLM-powered feature from a more complex agentic workflow.

2. Tool Calling and Agent Workflows

Learn the concepts behind connecting AI models to functions, APIs, and other software components. Tool calling allows an agent to retrieve information or perform supported operations rather than relying entirely on the knowledge contained in a model’s response.

Workflow design also involves deciding how steps connect, how results are passed between them, and how errors or unexpected responses should be handled.

3. Context Engineering and Memory

Effective agents need relevant information at the right time. Context engineering involves organizing the instructions, conversation history, retrieved information, and intermediate results provided to a model. Memory and retrieval techniques can help applications use information across interactions or access relevant knowledge from external sources.

4. Multi-Agent Systems

Explore the ideas behind coordinating multiple AI agents or specialized components. A system might divide a larger task into research, analysis, and writing responsibilities. Effective coordination requires clear roles, appropriate information sharing, and reliable control over the overall workflow.

5. Practical AI Projects

The publicly described course materials include end-to-end project work involving a research agent and an agentic writing pipeline. Projects of this kind help learners connect architectural concepts with working implementations, understand the trade-offs behind design choices, and develop practical experience with AI application development.

6. Evaluation and Observability

An agent can produce different results when given similar inputs, so testing only a few successful examples is not enough. Evaluation helps measure the quality of outputs and identify failure cases. Observability and tracing help developers understand the steps an agent took, investigate errors, and diagnose problems during execution.

7. Deployment and LLMOps

Moving an AI application into production introduces additional requirements, including configuration, authentication, persistence, automated testing, deployment workflows, and ongoing monitoring. LLMOps practices help teams manage these requirements and improve systems after their initial release.

The available course descriptions also reference tools and technologies such as LangGraph, LangChain, Gemini, FastMCP, and Google Cloud Platform. Exact lessons and tool versions may vary by course edition.

Who Is This Course For?

Agentic AI Engineering is particularly relevant to software engineers, machine learning practitioners, data professionals, and developers who already understand basic Python programming and language-model APIs. It may also help technical founders who want a stronger understanding of how production-oriented AI applications are built.

Because the subject involves software architecture, debugging, evaluation, and deployment, learners should be prepared for hands-on technical work. Complete beginners may benefit from learning Python, API fundamentals, and basic LLM concepts first.

Why Explore Agentic AI Engineering?

The value of agentic AI is not simply that a model can generate text. It is that carefully engineered systems can combine reasoning, external tools, structured workflows, and software infrastructure to solve more complex problems. Understanding these components can help developers build applications that are easier to test, maintain, and improve.

Paul Iusztin’s Agentic AI Engineering course provides a production-focused learning path for people who want to explore these engineering challenges through practical projects. It is a resource for developing stronger technical foundations in AI agents, workflow orchestration, evaluation, and deployment.

Frequently Asked Questions

1. What is Paul Iusztin’s Agentic AI Engineering course?
It is a technical training resource focused on designing and building AI agents, with an emphasis on practical engineering and production-oriented applications.

2. What is agentic AI?
Agentic AI refers to AI systems that can work through multi-step tasks, use tools, and follow workflows to achieve defined goals.

3. Do I need programming experience?
Yes, a working knowledge of Python and basic experience with APIs and language models will make the technical material easier to follow.

4. Does the course cover real projects?
Public course descriptions reference end-to-end projects, including a research agent and an agentic writing pipeline.

5. Which technologies are associated with the course?
Available descriptions reference LangGraph, LangChain, Gemini, FastMCP, and Google Cloud Platform. Check the official curriculum for the exact tools included in your edition.

6. Does it cover multi-agent systems?
Yes. Multi-agent system design is a central theme of the associated Agent Engineering course.

7. What are evaluation and observability in AI engineering?
Evaluation measures agent performance, while observability and tracing help developers understand execution and diagnose problems.

8. Is this course suitable for absolute beginners?
It is better suited to learners with some programming and LLM experience. Beginners should first develop the necessary technical foundations.

9. Can the skills be used in real-world applications?
The concepts can support research tools, writing pipelines, workflow automation, and other AI applications, although successful implementation depends on the specific project.

10. Where can I verify the official course details?
Visit Paul Iusztin’s official course website to confirm the current curriculum, enrollment options, pricing, and access terms.

Final note: Course availability, lesson content, software versions, and enrollment terms can change. Check the official provider’s current information before purchasing or publishing specific access claims.