Live support chatImplementing Agentic AI in Engineering
In Implementing Agentic AI in Engineering, you'll learn ...
- The principles, capabilities, and engineering applications of artificial intelligence and agentic AI
- The complete process for designing, implementing, and managing AI agents within engineering organizations
- The evaluation and mitigation of technical, ethical, cybersecurity, and operational risks associated with AI deployment
- How to establish governance, performance measurement, and implementation frameworks that ensure safe and responsible AI adoption
Overview
Preview a portion of this course before purchasing it.
Credit: 1 PDH
Length: 17 pages
Stay ahead of the curve with this comprehensive course designed specifically for licensed professional engineers. Implementing Agentic AI in Engineering delivers a structured, safety-first framework for understanding and deploying agentic AI in real-world engineering practice — covering AI fundamentals, autonomous agent design, risk and cybersecurity planning, workflow integration, KPI development, and governance policy. Grounded in the NIST AI Risk Management Framework and aligned with NSPE ethics standards, this course equips PEs with the knowledge to evaluate AI opportunities, manage implementation risks, and build compliant governance structures — all while upholding their professional obligation to protect public safety. Earn your PDHs while gaining practical, actionable skills for the AI-driven future of engineering.
Specific Knowledge or Skill Obtained
This course teaches the following specific knowledge and skills:
- The differences between traditional AI systems, generative AI, and agentic AI in engineering environments
- The identification and selection of high-value engineering use cases suitable for AI implementation
- The development of AI agent goals, requirements, performance criteria, and operational boundaries
- The design of autonomous and multi-agent systems using established AI frameworks and methodologies
- The assessment of data quality, bias, cybersecurity, ethical, and legal risks associated with engineering AI applications
- The application of the NIST AI Risk Management Framework to support safe AI deployment
- The integration of AI agents into engineering design, project management, quality assurance, and operational workflows
- The development and monitoring of key performance indicators that measure AI effectiveness, adoption, and business impact
- The creation of AI policies, governance structures, and accountability mechanisms aligned with professional engineering obligations
- How to develop a phased implementation roadmap for safely scaling agentic AI across an engineering organization
Certificate of Completion
You will be able to immediately print a certificate of completion after passing a multiple-choice quiz consisting of 12 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: 17 pages
