In Duly Noted, I warned of the allure of meta-work: the work we do to enable “real” work. Writing a paper is work; setting up and maintaining your writing environment is meta-work. Meta-work is important, but fiddling with tools is an occupational hazard. At least it was, until AI agents came along.
I’ve wasted countless hours testing new to do apps, distraction-free editors, Obsidian plugins, citation managers, read-it-later apps, etc. Each promises to unlock productivity. Some might, but in aggregate they don’t make up for the time wasted. Worse, meta-work creates the illusion of progress while accomplishing little.
AI agents change that equation. I’ve been migrating many of my workflows to agents running on Claude Code, OpenCode, and Codex. Setting up and optimizing them is meta-work, but it’s of a different kind than learning a new text editor. Investing in this kind of meta-work isn’t a waste of time.
For one, these “tools” (the agents) will be the ones doing the work (under my guidance, obviously). The more time I spend improving them can yield better work down the line. (I’m reminded of the (apocryphal?) Lincoln quote: “If I had eight hours to chop down a tree, I’d spend six hours sharpening my ax.”)
But there’s another reason why agentic meta-work is valuable: reflecting on my practice makes me a better practitioner. Building an agent to draw wireframes (for example) requires codifying techniques I’ve held tacitly for decades. Teaching an agent how to do it forces me to think about how I do it and why.
That’s a plus per se, but the resulting work is also different. I don’t just generate wireframes faster: the role of wireframes has changed. Before, I’d explore variations on paper before committing to Figma. Now, I can explore more possibilities at higher fidelity. Wireframes become a means for designing and not just communicating.
As I told a friend yesterday, I’m more excited about my practice now than I’ve been in over a decade. More of my work is evolving toward meta-work, but it’s valuable meta-work. Rather than fiddling with tools, it’s about architecting machine intelligence to better leverage my own.
This way of working requires a different approach. So far, the shift has taught me valuable lessons for steering agents. Here are three:
1. Assume Incompetence
Frontier LLMs are very powerful, but they won’t do things exactly as you’d like out of the box. You have to tell them, which will require codifying things you’ve taken for granted. Assume you’re dealing with a very capable intern who knows more than you do about many things, but not how you like things done.
You’ll need to explicitly state sequences of steps, intended outcomes, caveats, guardrails, and more. You’ll have to think about your work. How do you do things? Why do you do them like that? What’s the expected outcome? Get used to writing all this stuff down.
2. Plan for Continuous Improvement
But don’t assume it’ll happen in one go. There’s too much to capture, and the magnitude of the task might inhibit you. Instead, start with a minimal viable set of instructions and improve them as you see the agents’ results.
I end all my agent sessions with the following prompt:
What have we learned in this session?
When the agent responds, I ask it to selectively capture the resulting lessons either in the project’s AGENTS.md file or in the appropriate SKILL.md files. As a result, the agents’ understanding and abilities improve over time.
3. Structure the Context
As the previous lesson implies, agentic work requires structural distinctions. At a minimum, you’ll want dedicated directories for projects and system-wide agent skills. The former define particular contexts, whereas the latter define cross-context abilities.
For example, I have directories for individual client projects. These include information specific to each project: its goals, dates, actors, plans, content, structures, etc. But I also have directories for particular agent capabilities such as wireframing and building presentations, which apply across projects.
Everyone’s Now a Manager and a Teacher
The endgame isn’t automating myself out of the job. Instead, it’s allowing me to provide greater value to my clients. Drawing wireframes is a low-value activity. My time is better spent thinking about how to drive more sales or increase customer satisfaction or build a stronger brand presence.
I can’t provide high-value without low-value tasks, but that doesn’t mean I must do them myself. Agents can help, but they need supervision and instruction. We already know how to teach and manage people, and some of those skills transfer. But there are differences: working with AI requires more clarity and vigilance.
Whether your work entails design, business development, compliance, or a myriad other disciplines, more of your work will likely become meta-work soon. Consider where your work adds the most value and which parts can be automated. Then, start codifying the latter. The sooner you do it, the more prepared you’ll be to delegate.