What is an AI agent, and can your company use one? Where to start
An AI agent looks up data, works in your systems and reports back. To see if you could use one, start by asking an AI assistant: prompts to copy, four things to prepare, then start read-only.
Key takeaways
- Ask AI first: paste your daily tasks into an AI assistant such as ChatGPT, Claude or Gemini, have it pick the process best suited to an agent, then write down the steps and decision rules with your team.
- A chatbot answers questions; an AI agent breaks a goal into steps, uses tools, checks the results and works through the task until it's done.
- When you're ready, start with one process, a few tools and read-only access, running alongside the way you work now.
- Before an agent can change data, build in “propose, a person confirms, then execute” and a log of every step.
Table of contents
An AI agent is AI that does the work: you give it a goal, and it looks up the data, opens the systems, cross-checks and sums up, then reports back. Before it changes data or sends anything out, it stops and asks a person to confirm. It's best suited to work that means looking things up and copying between several systems every day.
Many teams already use AI assistants such as ChatGPT, Claude or Gemini to answer questions, draft copy and summarize documents. The natural next questions are: could our company use an agent, and would it touch data it shouldn't? This article starts with two prompts you can use today, then covers the groundwork, how agents work and how to start safely.
Ask AI first
You don't need an agent yet: an AI assistant can help you try two things today. Swap in your own details (the examples are illustrative).
Pick the best process to try first
Below are 8 tasks our sales assistant does every day, with roughly how long each takes and which systems it uses. Score each task 1–3 on four criteria: it repeats daily, the data is spread across several systems, the decision rules can be put into words, and a mistake can be caught before it causes damage. Show the scores in a table, pick the task best suited to hand to an AI agent first and say why, then break it into a draft list of steps.
Try the "look up and compare" part of an agent's job
Attached are this week's order list (order number, customer code, part number, quantity, promised date) and the shipping records (order number, ship date, quantity shipped). Compare them and list the orders past their promised date that haven't shipped or have only partly shipped. Give me a table with the order number, the quantity still outstanding and how many days late it is. Then draft a short, polite delay note to the customer.
The second prompt is what an agent would later do on its own; for now, you upload the files and decide whether to send the note. Check the AI's list against your own Excel filter, and tell it what it can't know, such as a customer who has agreed to a later date. Orders include customer names and prices: use your company's enterprise plan, or replace customer names with codes and delete unneeded columns before you upload.
4 things you can do now
None of these needs a new tool. Even if you never adopt an agent, they make the process clearer and easier to hand over.
1. Check the candidate process with the people who do the work
The process the AI picks is a starting point. Go through it with the people who do the work every day. Does it really repeat daily, in real volume? Is the data spread across several systems, such as orders in the ERP and conversations in LINE (the messaging app most Taiwanese customers use)? Can experienced staff explain how they decide? Can a wrong call be caught before it causes real damage? The more yeses, the better the fit.
Common candidates: a daily operations summary, answering common questions and checking order status in customer service (see AI for LINE customer service), reading the orders and quotes customers send and entering them (see reading orders and quotes with AI), and keeping website content and product data up to date.
2. Turn the draft into the real steps, then track a week
Ask the person responsible to turn the AI's draft into how the work is actually done: which system they open, which field they check, which rule they apply and what they do at the end, one line per step. The list is a handover document on its own. If you bring in an agent later, it becomes the agent's tool list and rules. Then track a week in a spreadsheet: how many items a day and how long each takes, so you can compare before and after.
3. Check whether software can read each system
Ask the vendor or whoever looks after each system: Is there an API? Can it export to Excel or CSV? Can you set up read-only accounts or role-based permissions? Does it keep an activity log? The answers decide how easy it will be to connect later.
4. Draw up a permissions table
Make three columns: can do directly (look up orders, check inventory), needs a person to confirm (create, change or delete records, send email, issue refunds) and not allowed. Then write down who reviews the log each week. Even without AI, this table makes clear who can do what.
Chatbots vs. AI agents: what's the difference?
The simplest way to put it: a chatbot talks; an AI agent acts.
| Chatbot | AI agent | |
|---|---|---|
| What you give it | A question | A goal |
| What it can use | What it learned in training, plus whatever you paste in | Your company's systems and tools (order lookup, inventory, email…) |
| How it works | One question, one answer | Breaks the goal into steps, carries them out, checks the results, then decides what to do next |
| What you get | An answer | A finished task, plus a record of what it did |
Here's an example. Ask a chatbot "Which orders are late this week?" and it can't see your order data until you export it and upload it. Ask an AI agent, and it will query the order system for this week's orders, compare them against the shipping records, pick out the late ones and give you a list. It can also draft a note to those customers, then wait for your OK before sending it.
How does an AI agent work?
Under the hood, an AI agent has three parts:
- A large language model: understands what you're asking for, plans the steps and judges the results.
- Tools: functions the model can call, such as "look up order," "check inventory," "create shipping order" or "send email." Each tool comes with a clear description, which is how the model knows when to use it.
- A loop: the model takes a step, looks at the result and decides on the next step, until the goal is done or it needs a person to step in.
That loop is the biggest difference between agents and traditional automation, which needs every path scripted in advance. When an agent hits something unexpected (a misspelled customer name, the same order entered twice), it first tries to sort it out, and only comes back to a person when it can't decide.
One reason agents have spread so quickly over the past year is open standards like MCP (Model Context Protocol). It gives AI a uniform way to plug into all kinds of systems, so you don't have to rebuild the integration for every AI tool.
When to consider the next step
With the groundwork done, it's worth letting an agent take over part of the work when:
- Every day, you look things up across systems, export files for AI and paste the results back.
- As orders and messages grow, the same lookups happen many times a day.
- You'd like managers to get a ready-made operations summary every morning, without waiting for someone to put it together.
- Your step list and permissions table are written, and the rules have been stable for a while.
The next step: start with one process, read-only first
An agent that can act can also make mistakes, so think it through at the design stage. A steadier approach is to go in phases:
- Read-only and small first: in the first phase, the agent only looks things up and summarizes, for example emailing the daily operations summary each morning. One process, three to five tools and a handful of users are enough. Your current way of working carries on, and you compare the two side by side for a week or two.
- Then "propose + confirm": the agent proposes what it plans to do, such as creating an order or replying to a customer, and the person responsible clicks Confirm before it runs. For the riskiest actions, you can even require typing a confirmation phrase.
- Hand off gradually: actions that have been confirmed many times without a single error can later run automatically. Deleting data or issuing refunds can keep a person's confirmation for good.
At every phase, two things stay in place: permissions follow the person (the agent works with the user's own permissions, so a sales assistant sees only their own accounts), and every step is logged (what it looked up, what it changed and who confirmed it, so you can trace problems and roll back to the version before the change).
Xena, the AI assistant from StudioX, is built this way: it always asks a person before changing data, every step is logged, and it can be set to read-only. When we bring an agent into a company's process, the first phase usually goes live in about 4–6 weeks.
Three common questions
"Will agents replace employees?"
In the rollouts we've seen, agents replace steps, not jobs: looking things up and copying and pasting go to the agent, while talking with customers, making trade-offs and taking responsibility stay with people.
"Isn't more automation always better?"
In practice, "AI does 90%, a person confirms the last 10%" is often worth more than full automation. That last 10% is mostly exceptions and high-risk cases; leaving them to people means fewer errors and more trust in the system.
"Can't we just buy an off-the-shelf agent?"
Off-the-shelf tools are fine for general work like writing up meeting notes. To save your company time, an agent has to connect to your own systems and be designed around your permissions, data formats and business rules. The step list and permissions table you wrote earlier are where that design starts.
To find out whether your processes are a good fit for an AI agent, start with a free AI Health Check, which gives you an automation blueprint. You can also look at our AI services, system integration and Xena, the AI assistant, or just talk to us.
