Four interconnected competencies necessary to ensure our interactions with AI are effective, efficient, ethical and safe. Fundamental knowledge for Domain 1 Agentic Architecture and establishing correct human-AI collaboration protocols.
AI Fluency is the ability to collaborate with AI in ways that are effective, efficient, ethical, and safe. It goes beyond simple "prompt engineering" tricks that become obsolete when new models release. Instead, it is a lasting framework of practical skills, knowledge, insights, and values that ensure success in an AI-driven environment.
To build AI fluency, we must move beyond treating AI as a simple tool. Depending on the task, we interact with AI across three distinct paradigms. None are inherently 'better', but Agentic Architecture (Domain 1) focuses heavily on Agency and Augmentation.
AI executes specific tasks based on your exact instructions. You give step-by-step commands and the AI completes a targeted chore. Best when you have a very clear outcome in mind (e.g., summarize a document, extract JSON).
Humans and AI collaborate as creative thinking and task execution partners. The AI doesn't just "do the work" for you; it helps you do your work better. Best when solutions aren't straightforward and you need a space to explore and experiment.
You configure AI to work independently on your behalf. Rather than giving exact directions for a specific action, you establish its knowledge base and behavior patterns. You act as a 'director' setting a vision rather than a 'scriptwriter'.
Regardless of whether you are leveraging automation, augmentation, or agency, these four essential competencies help you navigate collaboration optimally.
Setting goals and deciding whether, when, and how to engage with AI.
Delegation is about the big picture and strategically choosing how AI fits into your overall process. It requires understanding what you are trying to accomplish, recognizing what AI can and cannot do well, and thoughtfully dividing the work.
Effectively describing goals to prompt useful AI behaviors and outputs.
Description focuses on clear, context-rich communication. It goes beyond writing a basic prompt; it establishes exactly what you want to achieve, the output format, tone, and necessary context constraints.
Accurately assessing the usefulness of AI outputs and behaviors.
Discernment involves thoughtfully evaluating the output. It requires drawing upon your own domain expertise and critical insight to separate useful from useless, recognizing when outputs need refinement or should be discarded.
Taking responsibility for what we do with AI and how we do it.
Diligence focuses on responsible, safe, and ethical AI interactions. It means taking full ownership of your AI-assisted work and being willing to stand behind the final products you create.
Designing an Agentic System (Domain 1): When you build a multi-agent system, you Delegate tasks to different subagents. You write meticulous System Prompts (Description) so they know their boundaries. You build automated evaluators and human-in-the-loop gates (Discernment) to verify subagent outputs. Finally, you ensure the system respects permissions, secures data, and includes rollback mechanisms (Diligence).