Lately, one of the words heard most often in conversations about AI is "agent." Sometimes it's described as a digital employee that does everything on its own; sometimes it's dismissed as just another marketing label. The truth is somewhere in between. What are AI agents, how do they differ from a chat tool, and where can a business use them safely? In this post we explain the concept in plain language and walk through realistic use cases and limits.
The difference between a chat tool and an agent
If you ask a chat tool "What are this customer's open orders?", it can't answer unless you've given it that information, or it will guess. An agent receiving the same question connects to the order system, looks up the record, reads the result and summarizes it for you. If needed, it also takes the next step: it drafts the update email that will go to the customer.
Technically, an agent is a language model taking several steps toward a goal using the tools defined for it (ways of accessing systems). The model decides which tool to use and when, evaluates the result and carries on. In short: a chat tool talks, an agent gets work done.
Where do agents really help?
Agents create the most value in tasks that require gathering and combining information from several systems, and where the rules are largely known. Realistic examples:
- Reading an incoming quote request, looking up the company in the CRM, finding past proposals and preparing a summary for the sales rep
- Checking stock and order data and listing orders at risk of delay
- Reading a supplier invoice, comparing it with the purchase order and flagging the differences
- Classifying a support ticket, finding the relevant documents and drafting a reply
- Compiling a weekly management summary from several reports
What these tasks have in common is that the agent ends up producing a suggestion or draft, and a person makes the final decision. You can find a concrete example on the email side in our post on answering email with AI.
The limits of agents
Agents are capable but not flawless. The main limits you should know about:
- A wrong chain of steps. The model can misuse a tool, or take a faulty intermediate result as correct and carry on. The more steps there are, the higher the chance of error.
- Vague instructions. A broad goal like "take care of the customer" leads to unexpected behavior. Agents are more reliable on narrow, clear tasks.
- Content from outside. While reading an email or a web page, an agent can be influenced by hidden instructions inside it. This is called "prompt injection," and it's a security issue that must be taken seriously in agent design.
- Cost and time. Multi-step tasks use more computing power and time than a single chat answer.
That's why aiming for "an agent that does one job well" is healthier than "an agent that does everything."
Permissions: what can the agent touch?
The most important decision when setting up an agent is which permissions it gets. You wouldn't give a new employee the admin password to every system on their first day; the same logic applies to an agent.
| Permission level | What it can do | When it's appropriate |
|---|---|---|
| Read-only | Looks up records, reads reports | Initial setup, most analysis work |
| Draft writing | Prepares email, note and record drafts; doesn't send | Processes that run with human approval |
| Limited write | Updates specific fields (e.g. tag, status) | Tasks with clear rules that can be undone |
| Full access | Sends, deletes, initiates payments | Almost never, for a business |
With ERP and finance systems in particular, starting read-only is the safe choice. We cover this in detail in our post on read-only access when connecting a reporting tool to your ERP.
Pre-setup checklist
- The agent's task is written down in one sentence.
- The systems it will access and its permission level are defined.
- Irreversible actions (sending, deleting, payments) require human approval.
- Every step is logged, so the question "what did the agent do?" can be answered later.
- Content coming from outside is treated as untrusted data.
- The personal data flow has been assessed under KVKK (Türkiye's Personal Data Protection Law).
- A trial period and a success measure have been set.
How we do it at Globya
We start agent projects by mapping the work itself and the systems it will touch. In most cases, the real value of an agent is determined by the connections behind it: CRM, ERP, email, warehouse system. We build these connections as integration and custom software work and roll out the agent with the lowest possible permissions. Permissions expand only as the logs build confidence, and only with your approval. After the system is live, maintenance and improvement stay with the same team.
Frequently asked questions
Will an AI agent replace an employee?
In their current form, agents take over part of the repetitive, rule-based work. Decisions, responsibility and handling exceptions still stay with people; agents are more of a tool that speeds up the team's work.
Do our systems need an API to set up an agent?
Usually yes; an agent needs a connection path to access a system securely. If there is no API, alternatives such as a read-only database connection or file transfer can be considered.
Who is responsible if the agent makes a mistake?
The business using the agent. That's why it's important to require human approval for irreversible actions and to log every step.
Off-the-shelf agent products or a custom setup?
Off-the-shelf products may be enough for general office tasks. For work that connects to your own ERP, CRM or production systems, a custom setup is usually needed.
The Globya assistant is online 24/7; it answers right away and passes your question to the team if needed.