What is an AI agent?
An AI agent is software that uses a language model to decide which steps to take towards a goal and then carries out those steps with tools such as APIs, databases and email. A chatbot only answers; an agent looks up an order, opens a ticket or sends an email. Critical steps keep a human approval in the loop.
Definition
An AI agent is software that uses a language model to decide which steps to take towards a goal and then carries out those steps with tools such as APIs, databases and email.
How does an AI agent work?
At the centre of an agent is a large language model. The model is given a goal, a list of tools it may use and a set of rules. It chooses a step, for example calling a “get the customer’s latest order” tool, reads the result and decides on the next step. The loop continues until the goal is met or the case is handed to a person. An increasingly common standard for connecting tools to models is the Model Context Protocol (MCP).
When should you use an AI agent?
For work where input arrives as free text and each case unfolds a little differently: understanding a customer request and acting in the right system, updating an order from a supplier’s email, pre-screening an application against its documents. When the steps are fixed, a rule-based workflow automation is enough.
Building agents responsibly
Customer messages over WhatsApp Business, e-invoice lookups and marketplace order handling are common agent scenarios for companies operating in Türkiye. Because agents touch personal data, decide which tool may read which data under GDPR or KVKK, Türkiye’s data protection law, and log every action. For systems where several agents work together, see agentic AI.
Related terms
- Agentic AIAgentic AI is an approach in which AI systems do more than generate answers: they set sub-goals, plan, use tools and carry out multi-step work with limited human intervention.
- LLM (Large Language Model)An LLM (large language model) is an AI model trained on very large amounts of text that answers questions, summarises, translates and writes by predicting what text should come next.
- MCP (Model Context Protocol)MCP (Model Context Protocol) is an open protocol, introduced by Anthropic in November 2024, that gives AI applications a standard way to connect to tools, data sources and business systems.
- ChatbotA chatbot is software that converses with users in text or voice on channels such as a website, WhatsApp or in-app messaging, answering questions and handling simple tasks.
- Workflow automationWorkflow automation is an approach in which software carries out the repetitive steps of a business process, such as data transfer, approvals, notifications and reporting, according to defined rules.
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot produces replies in a conversation. An agent calls tools to reach a goal: it fetches data, creates records and performs actions in other applications. Many modern chatbots run as agents behind the scenes.
How much access should an agent have?
Enough to do the job and no more. Start with read access, require human approval for steps that involve money, contracts or customer communication, and log every tool call.
Are agents better than classic automation for every task?
No. When the steps are known in advance, a rule-based workflow is cheaper, faster and more predictable. Agents make sense when the input differs every time and the task requires judgement.
Let’s talk about your transformation
Tell us which work repeats every week. We will tell you honestly whether it is worth automating.
How we start
- A free first callWe pin down the problem and the goal together.
- A 2-week discovery sprintWe look at the data, the process and the tools, and decide where to start.
- A written proposalScope, architecture and timeline, in writing.