What is an agentic system, and how do you implement it in your system
2026-07-10
Anything that operates without your direct input may be called an agent: think of your morning reminder that fires every day. An agent is given a goal, and it determines the way to reach that goal for itself, using a combination of tools to read and act on information.
The agent’s tools
An agent’s ability to operate comes from a set of tools that allow it to read and write information. An agent is valuable when it begins interacting with the systems that your business already uses: CRM (the database where you store your clients’ information), calendar, tickets, and other systems you use.
Think of it as a new employee - on their first day, they can only use the systems available to them. Similarly, an agent only uses what you tell it to use.
Picture: agent in the middle, connected to the systems you want.
Given access to these systems, an agent can retrieve and update the information in them. For example, it could look through a client’s history in the CRM or reschedule a meeting in the calendar. However, to perform more complex tasks, an agent has to choose which of the available tools to use. This is the agent’s decision making process.
Implementing this against a real system
For any system, you can give the agent these tools to work with:
- A direct API, when one already exists
- Browser automation, like Playwright, driving the same screens a person would use
- An RPA style integration, the same kind many systems already have in place
Whichever of these already connects to your system, the agent uses that same path, no rewrite needed.
What’s different from classic RPA is where the decision gets made. RPA hard-codes the exact sequence of steps for every scenario in advance, the script does the same thing in the same order every time. An agent is given a menu of actions it’s allowed to take instead, plus whatever information it currently has, and it decides which action fits the situation in front of it. Here’s what that looks like in practice.
Real world example: transport management
Take a fleet tracking system like this one. It collects GPS points, mileage, and speed from many vehicles, and turns that stream into a live picture of where each vehicle is in real time. A fleet manager watches that live map today, and decides by hand whether a vehicle that strays off course needs a phone call or a schedule change.
An agent can take on that comparison step. Give it the same stream of points, plus each vehicle’s planned route, and it can compare the two continuously. When a vehicle strays from that route, the agent doesn’t act right away. It works out what’s likely going on and puts together a few options - keep going, call the driver, or adjust the schedule for the vehicles behind it - and sends them to the fleet manager.
The manager reviews the options and picks one. Only then does the action happen. The agent acts as an advisor, weighing the options and presenting the best ones for review.
Picture: the agent compares live points against a planned route and turns a deviation into options to confirm.
Building this doesn’t need a system rewrite. The GPS data and the reliable feed already exist. The agent is the added layer that compares it to a plan and turns a deviation into options a person can act on.
Can I use an agent for my task?
Not everything can or should be automated. Here is a list of questions to ask yourself:
| Question | Description |
|---|---|
| Does it require checking or changing information in a system? | Yes: an agent can be connected to the tools you already use |
| Can you add an approval step before a risky action goes through? | Yes: an agent is safe to use here, just require approval for the risky actions |
| Does the task change even slightly each time? | Yes: an agent can adapt, while a fixed script cannot |
| Do you need to know what actions were taken? | Yes: an agent keeps a record of what it did and why |
| Is the task a simple set of three steps always done in the same order? | No: a script is a better choice |
If you land on “yes” for the first four rows, and “no” for the last one, an agent is a good fit for your task.
Closing thoughts
In many ways, an agent is a simple model - it only has access to the same tools that you have given it. Running safely on its own takes more than that: rules for what it can decide alone, memory, and context over time. That extra layer is sometimes called the harness (think of it as the agent’s rulebook and short term memory). Its specific functions will be discussed in more detail in a later article.
FAQ
What differentiates an agent from regular automation?
Automations are usually sets of rigid instructions, while agents are given a general goal and have to determine the best way to achieve it on their own. An agent can also use different tools depending on the situation, while automations only have one course of action.
Do I need a special database for an agent?
No. An agent can run on the same tools you already have, including a simple spreadsheet. It may need special instructions for accessing your systems and the rules for operating with data.
Do I need to keep the approval steps for every action forever?
No. Many organizations begin with a strict set of checks and approvals for every action. As you become confident in your agent, you may reduce the number of steps for the least risky tasks.
Why is an agent more expensive than a simple script?
An agent is usually much more flexible and can perform a variety of tasks, while a script is limited to a single purpose. A single script may be cheaper, but an agent can serve many similar purposes without building a new one each time.