What's the difference between an AI agent and a chatbot?

A chatbot is a passive tool that responds as you ask questions; you have to be there every time. An AI agent, on the other hand, carries a defined task from start to finish on its own: it plans, gathers the information it needs, does the work, and calls you for review when it's done. The difference is the difference between 'question and answer' and 'delegating work.'

What tools does this system need?

Essentially three things: a task board (like Linear, Notion or Trello), an AI model that does the work (for example Claude/Fable), and a trigger that connects the two (a webhook). The tool choice isn't critical; what matters is the shape of the system: tasks collected in one place, the agent picking them up, and the result coming to a human for approval.

Can you fully trust an AI agent's output?

No, and that isn't the goal anyway. The agent does the heavy lifting, but a human makes the final call. In a good system the output first passes through a checklist (an eval); if it doesn't meet the criteria, the agent corrects itself. Then a human does quality control. This way speed and quality are preserved together.

Working With AI Agents: The New System That Runs Your Business at the Speed of Thought

Over the past few months, the way the most productive teams work has quietly changed. The reason is one thing: AI agents. But while everyone talks about "AI agents," very few show clearly what this looks like in a real workday. That's exactly what we'll do in this post.

The core idea is surprisingly simple: take AI out of being a box you ask questions to, and place it where your team tracks its work — right at the center of the business. Let's see how it works.

Taking AI Out of the Chatbox

Today most businesses use AI like this: they open a window, ask a question, wait for the answer, copy it. That's valuable but limited; because you have to sit there every time, and the conversation disappears once the work is done.

In the new approach, AI behaves like an "employee" that lives inside your task list. You define a job and leave it; the agent picks it up and carries it out on its own. The difference is the difference between "question and answer" and "delegating work."

The Four Parts of the System

A well-built AI working system is made of four complementary components:

Shared workspace.A task board where both people and agents work in the same place (like Linear, Notion or Trello). Tasks move through statuses: inbox → next → doing → waiting → done. It's not the tool that matters, it's this shape.
Capture.Getting every idea and task that comes to mind onto this board with the lowest possible friction. A hotkey, a phone button or a quick note — wherever you are, the work enters the system instead of staying in your head.
Evals.A standard checklist that every output the agent produces passes through before it reaches you. Output that doesn't meet the criteria isn't accepted; the agent corrects itself until it's within the bounds.
Quality control (QA).The final gate is you. The agent does the heavy lifting; you evaluate the options and apply your own taste and judgment.

How Does a Task Complete Itself?

The heart of the system hides in a single simple idea: marking a task runs a program automatically in the background. So the task isn't just a "note," it's also a "start" button.

The flow typically works like this: you write a task on the board and label it "agent ready." That label automatically sends a message (this is called a webhook — like a doorbell that rings when an event happens). The AI agent catches this message, reads the task, does the work, and when it's done moves the card to "waiting," calling you in for review.

Here's what's critical: during all this you do nothing. Your only action is defining and marking the task. On top of that, you can distribute dozens of tasks to different agents at once and run them all in parallel. This removes the need to sit at a screen all day queueing work — that is, it removes the biggest bottleneck.

How the Nature of the Work Changes

In this system your role changes fundamentally. You used to do the work yourself; now you spend most of your time correctly defining the work and evaluating the result.

In other words, you move up one level on the abstraction chain: it's not your hands but your mind doing the work. The bottleneck is no longer "how much work can I do in a day" but "how quickly and clearly can I define the work I want done." And that frees you to spend more time on the real levers — selling, building relationships, strategy.

Where Should You Start?

The most common mistake is trying to build a fully automatic system from day one. Going gradually is far healthier. First set up a simple task board, collect all the work in one place, and build the capture habit. Then define clear checklists (evals) for repetitive work.

Leave the fully automatic trigger layer (a webhook + an agent living on a server) for last; because that part needs some technical setup and budget. Building this integrated approach end to end for businesses is exactly what our AI automation service does. If you'd like to refresh the basics, take a look at our post on what AI automation is.

Conclusion

Working with AI agents isn't about "chatting faster"; it's about building an order where you can delegate work, no task gets lost, and you only make the final call.

Set up correctly, this system turns you from someone waiting at a screen into someone managing a fleet of agents — and the best part is, it keeps working even while you're busy with something else.