What a great time to be alive! AI tools and features are being released so quickly, too fast for most busy executives to keep up with. This article gives you a framework your brain can use to understand and file your knowledge about the tools that exist now and the new ones as they arrive.

Family 1 – Analysts: AI tools that analyze

For tools in this family, you chat with the AI. It can research a topic, summarize a long document, write a draft, pull out the key points, and work inside projects you have set up. For non-technical professionals, this is the most visible way to use AI as of June 2026. Think of this family as an analyst on your team. It studies things, reports back and then you decide what to do.

You will notice that many tools you already use have a built-in chat helper. When you ask that built-in helper to research or summarize, it behaves like a Family 1 Analyst, even though the chat feature is embedded in another program. The makers tend to label these helpers “Assistants.” Keep in mind that a real human assistant can take action for you, and that is where the next family comes in.

Family 2 – Assistants: AI tools that take action

You delegate tasks to AI, and it completes them. You can give these “task agents” selective access to your files, your mouse, and your screen, and they have connectors to other programs you use. Your instructions to a task agent can let it move a file, send an email, write a row in a spreadsheet, add a record to a database, notify your team, and more. Instead of dragging a dozen documents into a Family 1 Analyst and asking it to do a task, your task agent can find the dozens of files itself and do the work using those files, based on your instructions. The difference in this family compared to Family 1 is that here you end up with a completed task, something a task agent did for you based on your instructions right then. The next family lets you create workers that run entire workflows for you or serve as highly sophisticated task agent workers that can run automatically when needed.

While using AI in Family 1 carries privacy and security risks, Family 2 requires even more attention. Don’t be afraid to use these tools, but approach them carefully and put protections in place. You must accept some risk in order to use these tools. “Cloud task agents” that run in the cloud put you at risk if an attacker can find a way to exploit weaknesses in them by using techniques such as “prompt injection” to trick your AI task agent into working for them. One goal threat actors have is to trick your task agent into sending them sensitive information. Once you start using “on your machine” task agents that might have access to your local computer, including accessing some files on your drives and the ability to imitate you by moving the mouse and clicking the mouse buttons, based on what it “sees” on your screen, your risk increases. If your AI behaves irrationally, or an attacker is able to take control of it, you’re more exposed.

Family 3 – Tools that let you create workers

This family is where you build highly skilled workers who can start on their own at an event, such as when an email arrives or at a set time of day. You manually start the Family 2 tools. Family 3 helps you produce task agents that can start automatically, without you needing to be present.

There are two kinds of workers you can make here. The first is a workflow in which you lay out every step yourself, so the result is predictable and repeatable. AI reasons on its own, so you will not always get the same result if you use AI within a workflow. When you add an AI step to a workflow, it can return different results each time, and that variation can disrupt the operation of the otherwise predictable steps that follow. Great news: workflows can be composed of steps that do not have to use AI at all, so the workflow is predictable, which is essential for work that must be accurate every time, such as exact statistical or financial calculations. AI can help you design your workflow, but stay out of the operation when you run the workflow. You can still invite AI into a step when it helps, like having it summarize the result at the end, and you can leave AI out entirely when you want pure, predictable math and other behavior.

The second type of AI in this family is a task agent builder. Instead of writing out every detailed step, you give the worker a goal and let it work out the steps on its own. You design a worker that you will not tell what to do; you just give it an outcome to achieve. This differs from the step-by-step workflow described in the previous paragraph because with the task agent builder, you build an AI worker that has the flexibility to decide its own steps each time it runs.

Both kinds run automatically when an event occurs, such as an email arriving, and both let you hand off tasks you used to do manually. The difference is whether you want to define the steps or let AI choose its own steps to achieve your result. The first can be predictable if you leave AI out of the steps, and the second can be fluid, flexible and adaptable, but be prepared that you might not always get a result you expected.

Family 4 – Tools that let you write programs

With these tools, you explain a program in plain English, and the AI writes it for you. This activity is called vibe coding. AI helps you add features and upgrade your program whenever you want, without you needing to learn how to program. Experienced developers use this family too, to speed up their own work.

There are two kinds here. The first kind, called app builders, write the program and host it for you in their cloud, so you stay in plain English from start to finish. You won’t need to understand much about how programs work on the backend.

Other tools, called agentic coding tools, write code you can run wherever you like, giving you more power and showing you more of the moving parts. You’ll have an opportunity to get a little deeper into what is going on, and the AI tool can help you through the process. Having the flexibility not to be locked into a specific vendor’s cloud can be appealing in some cases.

Categories within the families

To help your mind sort tools more quickly and remember them longer, there are groups of tools inside those families. Here are some example tools available now (June 2026) and where they fit. We do not endorse any of these tools, nor do we recommend or recommend or advise against any of them, although we do use many of them. We feel that giving you the product names can help solidify the differences in the families for your memory.

Family 1 – Analysts

• General chat analysts: Claude, ChatGPT, Gemini, Perplexity, Grok
• Customized analysts: Claude Projects & Skills, Custom GPTs & GPT Projects, Gemini Gems, Perplexity Folders & projects, Microsoft Copilot agents, Grok automations

Family 2 – Assistants (your task agents)

• Cloud task agents: ChatGPT Agent, Gemini Spark, Perplexity Computer. As with everything in all of these families, be aware of privacy and security risks.
• On-your-machine task agents: Claude Cowork, Perplexity Personal Computer, OpenClaw, NanoClaw. Be especially aware if you use these task agents; they can carry enormous risks in some cases.

Family 3 – Tools that let you create workers

• Workflow automation: Zapier, Make.com, n8n, Gumloop, Microsoft Power Automate
• Agent builders: Zapier Agents, OpenAI Agent Builder & Agents SDK, Botpress, StackAI

Family 4 – Tools that let you write programs

• App builders: Base44, Lovable, v0, Replit
• Agentic coding tools: Claude Code, Codex App, Cursor. The AI built into developers’ code editors lives here too, such as GitHub Copilot, VS Code AI, and JetBrains AI.

Terminology

Now that you have the families in mind as a framework, you will benefit from some terminology.

Agent – The term “Agentic AI” refers to AI that can take action, and the word “agent” always benefits from a descriptor next to it, such as “coding agent” for an agent that writes code, “task agent” for an agent that performs tasks, and so on.

Embedded AI – This is when software you already own has AI features built in, such as a chat helper in your email or a spreadsheet. Usually, embedded AI is a feature you enable, not a separate tool.

Connections – Connectors provide access. This is how programs connect to other programs you use, online services, databases, and everything else. For AI to work in the real world, and to reach the data sitting in your databases and elsewhere, you need connectors. You may see the terms API and MCP; I will cover them in a future article. They are the backbone of most connectors that provide access. Access by itself is not enough, though. The tool also needs to know what to do with that access, which leads to the next term below, skill.md. Connectors carry a significant risk if a threat actor compromises one. We call this “east-west” security because it involves data flowing between programs, as opposed to the traditional “north-south” security that protects your data and systems via a firewall. Using connectors bypasses firewall protection because your SaaS applications can communicate with each other without the conversation ever passing through the traditional firewall at your network perimeter, where your network connects to the outside world. This east-west traffic is harder to see and control than traditional perimeter traffic, and it should be on your CISO’s radar, especially if workers set up connections without their knowledge or approval. Threat actors target connectors. I will cover service-to-service, API, and MCP security inside and between environments in more detail in a future article.

SKILL.md – This is a file that teaches AI how to do a task the way you want it done. The skill file often includes instructions on how to work with another program you have connected to, and it can also hold your own process, such as your style, checklist, or standards. The connector gives the AI access; the skill file gives it the know-how to do a great job. You do not need to remember this, but the “md” in the file name stands for “markdown,” and md files are saved as plain text you can read and edit in a basic app such as Notepad or TextEdit. People often say “skills” out loud, while the file itself is usually named SKILL.md. Just as you train a new worker at your organization, you can use a skill file, along with related markdown files, to train your task agents and other AI tools.

AaaS – Agent as a Service is a way you can pay for task agents to perform specific tasks for you. Their features fit in Family 2 above, and they are useful when you just want to pay for a result. For example, you might pay a monthly fee for a task agent to run your lead follow-up and clean up your sales pipeline.

Loops – Looping is a recursive process in which the AI plans, acts, observes, and refines, then repeats the cycle, starting with refined planning. Each pass through the loop can improve the result. Keep in mind that more loops do not always mean a better answer; the gains usually are higher during the first rounds. As of now, a loop can drift in the wrong direction if it is unsupervised and runs too many times. Looping also uses a lot of computing power, known as “compute,” which can mean a high token cost, the next term.

Tokens – Companies such as Google, OpenAI, and Anthropic charge you to use their models, and the unit they use to measure usage is called a token. To give you a rough idea, a token is about three-quarters of a word in the English language. If you are using a Family 1 chat tool for a monthly fee, you usually are not billed by the number of tokens you use, but you might find yourself temporarily restricted if you reach a specified limit. The other families may have features that result in your getting charged per token. You use more tokens when you run more activities, open larger files, and run processes more often. You are charged for both what you send to the model and what it sends back to you. The topic of saving money with AI while being charged per token deserves special attention, because some companies are finding AI is becoming very expensive for them. I will write an article about that soon.

Conclusion

Now you have some helpful terminology and, more importantly, a framework your memory can use to file AI tools into families. Share this with your friends so that, as new AI tools arrive, and they will keep arriving quickly, they can file each tool into its family and help keep their sanity while everything else keeps changing.