Design for Agility
AI technologies evolve at a pace unlike previous workplace tools, new capabilities, platforms, and use cases emerge every few months. AI literacy cannot be treated as a fixed curriculum. Training must be designed with built-in mechanisms for adaptation so content and delivery stay current with the technology landscape.
"Design for Agility" is the seventh delivery principle of the DOL AI Literacy Framework (TEN 07-25). Because "AI technologies evolve at a pace unlike previous workplace tools," it requires training built with adaptation mechanisms: continuous content updates, feedback-driven iteration, modular content design, responsive use-case selection, and outcome-driven iteration.
Last verified against the DOL source: August 20, 2026. What changed
Score your program on these five checkpointsWhat the framework says
Paraphrased: new AI capabilities, platforms, and use cases "emerge every few months, while older tools become obsolete just as quickly." AI literacy therefore "cannot be treated as a fixed curriculum"; training must have "built-in mechanisms for adaptation so content and delivery stay current with the technology landscape," so workers leave "with skills that match the tools they will actually encounter on the job."
The five example delivery approaches:
- 7.1
Continuous content updates
Building delivery systems that allow for regular refreshes of tools, examples, and instructional content to reflect current AI capabilities.
- 7.2
Feedback-driven iteration
Using learner input and real-world outcomes to revise delivery methods and content based on what is working in practice.
- 7.3
Modular content design
Structuring training in flexible units that can be swapped, expanded, or reordered as new needs or technologies emerge.
- 7.4
Responsive use case selection
Revisiting and revising scenarios periodically to ensure alignment with the latest workplace applications of AI.
- 7.5
Outcome-driven iteration
Evaluating whether participants are gaining practical, transferable AI skills and using those insights to adapt and refine delivery strategies.
Reproduced from the public framework text under 17 U.S.C. section 105. Read the source on dol.gov.
What it means in practice
This principle is a procurement weapon. Any fixed-content AI course begins decaying the day it is recorded; the framework hands buyers the question that exposes it: when was this content last updated, and what is your update mechanism? Note that "modular content design" is an architecture requirement, units that can be swapped without rebuilding the course, and "outcome-driven iteration" is a measurement requirement: the program must evaluate whether learners gain "practical, transferable AI skills" and change in response. A vendor with no answer to either question fails Principle 7 regardless of content quality today.
How programs fail this provision
Recorded-in-2024 failure: screenshots of dead interfaces, exercises for deprecated models, no changelog, no refresh commitment. Second failure: updates that are promised but unverifiable, no visible revision history. The framework's standard is a mechanism, not a promise. (It is also why this guide carries a verification date and why DOL calls its own framework a living document.) Inspection question: show me your changelog.
The five checkpoints this provision scores on
There is a standing mechanism for regularly refreshing tools, examples, and instructional content as AI capabilities change.
Framework marker: Continuous content updates
Learner feedback and real-world outcomes are systematically used to revise delivery.
Framework marker: Feedback-driven iteration
Training is built in modular units that can be swapped, expanded, or reordered as needs and technologies change.
Framework marker: Modular content design
Scenarios and use cases are periodically revisited for alignment with current workplace AI applications.
Framework marker: Responsive use case selection
The program evaluates whether participants gain practical, transferable AI skills, and adapts based on the results.
Framework marker: Outcome-driven iteration
How GAGE addresses it
In AI Literacy and Professional Conduct, this provision sits in Assessment and Continuous Learning and Agentic AI and Workforce Integration. Every topic in the program ends in a mastery assessment, and the teach-back gate asks the learner to explain the material back in their own words before it counts as understood, which is the difference between a program that covers a checkpoint and one that can evidence it.
Module titles come from the program registry, so this cannot drift away from what the program contains. GAGE is a private company: the program is built to the DOL framework, and DOL neither certifies nor endorses it or any other program.
Questions about this provision
Why does the framework stress agility so heavily?
Because AI "evolve[s] at a pace unlike previous workplace tools", a fixed curriculum teaches tools workers will never meet. Agility is framed as a design requirement, not a marketing feature.
What does "modular content design" mean for buyers?
Training built in swappable units, so individual topics update without rebuilding the course, ask vendors to demonstrate one module that changed in the last quarter.
Does your program actually meet this provision?
Answer the five checkpoints above and the other 55, and get a dated report that scores your program provision by provision. Free, and your answers stay in your browser.
What this means for employers
TEN 07-25 for Employers: Building an AI-Literate Workforce to the DOL Framework
GAGE (Global Academy of Generative-AI Education) is a private education company, not affiliated with or endorsed by the U.S. Department of Labor. DOL does not certify or endorse training programs. Framework summaries are drawn from the public TEN 07-25 document. Read TEN 07-25 on dol.gov. Last verified: August 20, 2026.
Last verified against the DOL source: August 20, 2026. What changed