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Paul Iusztin – Agentic AI Engineering
Agentic AI Engineering is a hands-on technical training designed for developers, engineers,
and builders who want to go beyond passive AI tools and design real autonomous systems that operate in production.
Instead of teaching only how to ask questions of large language models,
this program focuses on how to create, test, and deploy agentic AI workflows and agents that actually reason, act,
and automate tasks in real-world applications.
Understanding Agentic AI Engineering
At its core, Paul Iusztin β Agentic AI Engineering teaches you how to build AI systems that:
Perceive context, not just respond to prompts
Plan actions by reasoning about tasks
**Execute multi-step flows across tools, APIs, and systems
Operate independently rather than only generating text
This is a step beyond basic chatbot creation β itβs about turning intelligence into actual execution.

From Design to Deployment
Knowing how to build an agent is only part of the challenge β the program emphasizes production readiness:
Testing methodologies for autonomous agents
Debugging multi-step workflows
Tool use and external API integration
Deployment and runtime maintenance
These components ensure your AI systems arenβt just prototypes, but deployable, reliable tools.
Who This Training Is Best For
Software developers and machine learning engineers
AI practitioners transitioning from passive models to autonomous systems
Product builders and technical founders working with LLM-based automation
Engineers who want production-ready AI skills rather than isolated demos
What unites learners is a desire to create systems that do work, not just talk about it.
What Makes This Approach Distinct
Unlike courses that focus only on generative text or prompt craft, the course goes further by teaching:
How to build reasoning loops
How to combine models with external tools and infrastructure
How to evaluate and iterate on agent behavior
How to scale autonomous workflows
This is training tailored for engineers who want to go from concept to deployable systems in an AI-driven world.





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