The latest AI flex is how many AI agents you’re running. I’ve seen people talking about running everything from 5 agents to a whopping 32 agents, a humble brag that’s about as impressive to most as discussing your Fantasy Football line-up.
If you’ve been building an AI strategy for your business there’s a good chance you’ve already made the mistake this article is about. You built too many AI agents to do jobs that are almost identical. Each one needed its own setup, its own upkeep and eventually each broke in its own way on the next platform update and you had to figure out why.
What you actually needed was one agent and three skills.
The difference between AI Agents and AI Skills
AI agents are autonomous system that uses tools, access, resources, and makes decisions to take actions that achieve a specific goal.
Think of an AI agent like hiring a new employee for every single job that comes through the door. Each one gets their own desk, their own training, their own set of tools, and their own quirks you have to manage. If three jobs come in that are 90% the same, you still end up training and managing three separate people.
AI skills are a specific set of instructions. You hire one capable employee (your main agent), and you hand them the manual that matches whatever job is in front of them today. Same employee. Same tools. Same job. Slightly different instructions depending on the task.
The employee doesn’t change. The instructions do..
AI Agents vs. AI Skills: When to use each
Build an agent when the work genuinely requires independent judgment.
- The task has multiple steps that depend on each other. Research a topic, draft an outline, write the piece, then publish it. Each step changes based on what happened in the last one.
- It needs tool access to get the job done. Pulling data from an API, searching the web, running a calculation. The AI has to go get something it doesn’t already have.
- You’re handing over a goal, not a process. You say “figure out why this client churned” and let it work out the steps on its own, instead of walking it through each one.
- When you need to run simultaneous tasks. One agent can run one task at a time. Multiple AI agents can work simultaneously.
In my Samaritan OpenClaw build I created a dedicated Signals Agent whose job is to research specific things, using tools with specific access that the other agents have no business touching, including API keys that only it is allowed to access.
That’s not a different instruction sheet. That’s a different employee with a different job, different set of permissions and a different toolbox. to carry out it’s mission.
AI Agents Pros and Cons
The pros:
- Customization: A custom agent can be built around your specific data, your specific systems, and how your business actually runs.
- It can take real action: A well-built agent doesn’t just suggest what to do. It can do it, end to end, without you sitting there clicking through each step.
- You keep your data in house: Building your own agent means you control where your information goes and who can see it.
The cons:
- Maintenance: Every agent you build is something you now have to maintain. When the AI model behind it changes, someone has to check that it still works.
- Vendor lock-in: Agents are generally built on the agentic platform. The more memory, history, and custom setup an agent accumulates on one platform, the harder it will be to leave that provider. What started as convenience turns into dependency, and switching later gets expensive and painful.
- It can behave in ways you didn’t plan for: AI is still experimental and will make mistakes. Without tight guardrails an agent looking for the easiest path through a task can find a shortcut you wouldn’t have approved of.
- API Cost: Agents that think through every step use more resources than a simple set of instructions does. If you’re running agents for hours or multiple agents at a time, that is going to add up quickly especially if you’re running a token hog like Claude Cowork.
When you actually need an AI Skill
Build a skill when you find yourself prompting the same tasks over and over. Skills are for repeatable tasks that doesn’t change much. That means:
- The input and output are predictable. Summarize this document. Pull the names and dates out of this contract. Write this in my brand voice for this specific audience.
- You want to stay in control of each step. You’re not handing off the whole job. You’re guiding it, and you want consistent results every time you ask.
- The task doesn’t need the AI to think through twenty possible paths. It needs to do the one thing, fast, the same way every time.
For instance, every single AI agent I run needs to know how to research using Google’s advanced search operators (commonly called Google Dorks). So instead of teaching that skill to each agent separately, or building a dedicated agent just to do searches for everyone else, I built one Search Ninja skill that every agent can use. One instruction manual. Many employees using it.
I recently spoke with a business owner who built 3 different AI agents to write articles for 3 different niches and target audiences. 3 agents, 3 X’s the unnecessary complexity and moving parts. I showed them how to take each AI agent’s instructions and targeting and create 3 AI skills instead. Now one agent has access to the rules of which ever audience and voice it needs to create for.
AI Skills Pros and Cons
The pros:
- AI Skills are fast to build and fast to fix: A skill is an instruction sheet, not a piece of infrastructure. Updating it is as simple as editing a document, not rebuilding a system.
- You’re in control: You’re guiding the process at each step instead of handing the whole thing over and hoping for the best.
- AI Skills are portable: When built properly, or as part of an AI OS, skills can be used across most agentic platforms, which means you’re not locked into a specific platform to use your skills.
The downside:
- It only works as well as it’s written: A vague skill produces vague results, every time.
- It’s not built for judgment calls: If the job needs the AI to weigh options and adapt mid-task, a skill alone will fall short. That’s an agent’s job.
- You have to keep the current: If your brand voice, offers, or your process change and the skill doesn’t get updated, it keeps producing yesterday’s answer.
Final thoughts on AI Agents vs. AI Skills
These days I only build agents when absolutely unnecessary for many reasons:
- Every agent built is something you now own and have to maintain. Three agents means three separate things that can go wrong whenever there’s a platform update that now calls a function differently than how it was done when you built your agents.
- The vendor lock-in problem is real. I like the flexibility of using my entire AI OS on any agentic platform and I can’t do that if my agents are on one and not the other. The longer your agent builds a history on a platform, the harder it is to move it somewhere else.
I recently converted that Signals agent into a skill. Yes, it’s a robust skill that needs an API key for one of the resources, but I can take it anywhere and am not locked into needing to log into my OpenClaw installation to use it.
I’m actually in the process of converting as many AI agents as possible into skills that can be added to my AI OS because the benefits are so great. You build the skill once, and it works platform wide. You can read more about how that works on the AI OS page.
This doesn’t mean agents are a bad idea. When a job genuinely needs independent reasoning, its own tools, and its own permissions, an agent earns its complexity. The mistake isn’t building agents, it’s building a new one for every single function because someone told you that you needed to build a fleet of agents.
You don’t get points for adding more complexity. You actually get points for building a functional system that’s easy to maintain, and works as expected.
Before you build anything new, ask yourself: is this a different job, or is it the same job with different instructions?
- If it needs its own tools, its own permissions, or its own judgment calls, build the agent.
- If it’s the same task wearing a different hat, write the skill.
AI Consulting and Support Specialist
Sec+ CySA+
I show SMBs how to leverage AI to save time, cut costs, and automate tasks. Let’s do a free 30 min chat via Google Meet and see if we can start turning your AI problems into solutions.
