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Generative AI vs. Agentic AI in Engineering
In Generative AI vs. Agentic AI in Engineering, you'll learn ...
- The fundamental differences between Generative AI and Agentic AI in engineering applications
- The integration of AI-driven content creation and autonomous decision-making within engineering workflows
- The benefits, limitations, and risks associated with deploying AI technologies in engineering practice
- How to implement AI tools responsibly while maintaining professional, ethical, and safety obligations
Overview
Preview a portion of this course before purchasing it.
Credit: 1 PDH
Length: 14 pages
This course is designed for licensed engineers seeking to understand and responsibly apply today's most impactful AI paradigms. The course clearly distinguishes between Generative AI—which produces designs, code, and content on demand—and Agentic AI—which autonomously plans and executes multi-step tasks to achieve engineering goals.
Through real-world applications across civil, mechanical, electrical, and software engineering disciplines, participants learn how each technology can accelerate workflows, where each carries risk, and how to apply the ethical and professional standards that govern their use. Practical guidance on validation, human oversight, accountability, and bias awareness equips engineers to integrate AI tools confidently and competently. Whether you're exploring AI-assisted design or deploying autonomous control systems, this course provides the foundational knowledge to lead with professional judgment.
Learning Objectives
Upon completion of this course, participants will be able to:
- Describe the core characteristics and operating principles of generative AI systems.
- Explain the perception, reasoning, action, and learning cycle that defines agentic AI operation.
- Identify applications of generative AI in civil, mechanical, electrical, and software engineering disciplines.
- Describe the use of agentic AI for automation, infrastructure management, robotics, and systems control.
- Explain the advantages of AI-assisted design exploration, optimization, and workflow efficiency.
- Identify the limitations of AI systems related to validation, transparency, bias, and reliability.
- Distinguish between content generation and autonomous goal-directed behavior.
- Explain the relationship between generative AI and agentic AI within integrated engineering solutions.
- Describe the ethical responsibilities associated with safety, accountability, privacy, and intellectual property.
- Establish validation, monitoring, documentation, and governance practices for engineering AI systems.
Certificate of Completion
You will be able to immediately print a certificate of completion after passing a multiple-choice quiz consisting of 10 questions. PDH credits are not awarded until the course is completed and quiz is passed.
| This course is applicable to professional engineers in: | ||
| Alabama (P.E.) | Alaska (P.E.) | Arkansas (P.E.) |
| Delaware (P.E.) | District of Columbia (P.E.) | Florida (P.E. Area of Practice) |
| Georgia (P.E.) | Idaho (P.E.) | Illinois (P.E.) |
| Illinois (S.E.) | Indiana (P.E.) | Iowa (P.E.) |
| Kansas (P.E.) | Kentucky (P.E.) | Louisiana (P.E.) |
| Maine (P.E.) | Maryland (P.E.) | Michigan (P.E.) |
| Minnesota (P.E.) | Mississippi (P.E.) | Missouri (P.E.) |
| Montana (P.E.) | Nebraska (P.E.) | Nevada (P.E.) |
| New Hampshire (P.E.) | New Jersey (P.E.) | New Mexico (P.E.) |
| New York (P.E.) | North Carolina (P.E.) | North Dakota (P.E.) |
| Ohio (P.E. Self-Paced) | Oklahoma (P.E.) | Oregon (P.E.) |
| Pennsylvania (P.E.) | South Carolina (P.E.) | South Dakota (P.E.) |
| Tennessee (P.E.) | Texas (P.E.) | Utah (P.E.) |
| Vermont (P.E.) | Virginia (P.E.) | West Virginia (P.E.) |
| Wisconsin (P.E.) | Wyoming (P.E.) | |
Preview a portion of this course before purchasing it.
Credit: 1 PDH
Length: 14 pages
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