Over the last six months I have spent a lot of time building with AI agents. Not just asking a chatbot for snippets, but actual working loops: code changes, plans, reviews, debugging sessions, memory updates, website edits, long-running build tasks, and the occasional confident walk into a wall.
I expected the main skill to be prompting. It wasn’t.
The skill is management.
The better these tools get, the less the bottleneck is whether the model can do something useful at all. The bottleneck moves back to the human: knowing what matters, defining the work clearly, giving the right context, checking the result, and deciding what happens next. A good prompt can start work. It does not manage work.
Prompting was the first interface
Prompting mattered because it was the first way most of us learned to talk to these systems. You typed a question and got an answer. Then you asked for code, or an outline, or a better version of an email. That was useful, and it still is.
But once you move from asking questions to getting work done, prompt craft is only a small part of the job. The real loop looks more like this:
- What are we trying to finish?
- What does the agent need to inspect first?
- What is in scope, and what is not?
- What would count as done?
- How will we verify it?
- What should be saved so the next run starts smarter?
That feels less like prompting and more like managing a small team. A strange team, admittedly: very fast, very patient, sometimes brilliant, sometimes weirdly literal, and occasionally overconfident in exactly the way you do not want. But still a team of sorts.
Everyone becomes a manager
For a long time, management was something you grew into after doing the work yourself for years. You wrote code, then managed developers. You wrote copy, then managed writers. You ran campaigns, then managed marketers.
AI bends that path. A solo founder can now have a coding assistant, a research assistant, a writing assistant, a design assistant, and an operations assistant open in the same afternoon. They do not have employment contracts, Slack accounts, or coffee preferences, but the management problem is still there. Someone has to decide what matters. Someone has to break the work into pieces, and notice when the output looks right but misses the point. Someone has to say “no, that is too broad”, or “show me the diff”, or “run the test”, or “this is technically correct but wrong for the customer”. That someone is still human.
This is going to be strange for a lot of people, because many of us will become managers before we feel like experts. A small business owner might manage a marketing agent before they understand marketing deeply. A designer might manage coding agents. A founder might run research, sales, engineering, content, and finance workflows from the same desk.
That is empowering. It is also risky. If you cannot tell good work from bad work, AI gives you more bad work faster. If you cannot describe the outcome, AI gives you polished wandering. If you cannot prioritise, AI gives you motion without progress.
The job is not to become a prompt wizard. The job is to become a better manager of work.
The agent can move fast. Direction still matters.
The best results I have had with agents come from a boring loop. Make the goal smaller. Ask the agent to inspect before it changes anything. Make it explain what it found. Give it permission to change one bounded thing. Run the build, the test, the diff, or whatever real-world check applies. Then review the result.
That loop is not glamorous. It is also the difference between useful agentic work and a mess that looks productive until you touch it.
When things go wrong, it is rarely because the model was useless. It is because the work was badly managed: too much scope, no source of truth, no acceptance criteria, no verification, and nobody checking whether the output actually solved the problem.
Agents make bad management look productive for longer. That is the part I keep coming back to.
Self-improving work is mostly better process
The most interesting loop I have found is not getting an agent to do a task. It is getting the system to learn from the task. If an agent hits a problem, update the checklist. If a workflow repeats, turn it into a reusable skill. If a test catches a bug, keep the pattern. If an instruction keeps producing the wrong shape, fix the instruction instead of correcting the output forever.
This is where agentic work starts to compound. Not in some grand recursive intelligence story, just normal operational learning applied consistently:
- Do the work.
- Notice what broke.
- Improve the process.
- Start the next run with a better system.
That sounds obvious, but most teams do not do it well with humans either. The difference with agents is that the feedback loop can be much tighter if you build it deliberately. A good agent setup should not just produce output. It should leave behind better instructions, better tests, better docs, better memory, and better boundaries. Otherwise you are asking a very fast worker to make the same mistake again tomorrow.
Judgment gets more valuable, not less
This is where I think some of the AI conversation goes sideways. People talk as if better models will remove the need for judgment. I am seeing the opposite. The more capable the tools get, the more judgment matters: what to build, what to ignore, what is good enough, what needs a human approval, which facts need checking, which system owns the truth, and when the agent should stop and ask.
Those questions do not disappear because the model got faster or the context window got bigger. If anything they get sharper, because the cost of vague thinking drops. You can now turn a half-formed idea into a lot of work very quickly. That can be useful. It can also be weaponised wandering.
Where I land for now
I do not think the future belongs to the people with the fanciest prompts. Prompts matter, but they are a small part of the skill. The advantage goes to people who can manage work clearly: define outcomes, give useful context, split tasks sensibly, verify against reality, and keep what they learn.
That applies whether the agent is writing code, drafting copy, researching a market, designing a workflow, or helping run a business process. The tools are becoming more capable.
That makes management the skill.
If you are building your own loops with agents, I would love to hear what management looks like for you. Feel free to reach out.