What is an AI agent?
An AI agent is software that uses a language model to understand a goal, plan the steps to reach it and carry out those steps with tools, such as looking up data, creating a work order or drafting an email. The agent checks the result and asks a person for approval when that is required.
The key difference with traditional automation is flexibility. A script follows fixed rules. An AI agent can handle unstructured input, such as an email or a PDF, and decide which step comes next within the limits you set.
How does an AI agent work?
Most AI agents follow the same loop. The agent receives a goal, for example "process this order from the customer's email". It interprets the request, makes a plan, calls the tools it needs, checks whether the result is correct and then reports back or escalates to an employee.
That loop repeats until the task is done. Because every step and every tool call is logged, you can always see what the agent did and why.
The building blocks of an AI agent
A production-ready AI agent is more than a language model with a prompt. It consists of five parts that together determine how useful and how safe the agent is.
- Language model: the "brain" that understands the request and reasons about the next step, for example a model from Anthropic (Claude) or OpenAI.
- Instructions: the role, rules and expected outcome, written down as clearly as a work instruction for a new colleague.
- Tools: controlled connections to your systems, such as your ERP, CRM, email or document storage.
- Context and memory: the customer data, history and company knowledge the agent needs to do the task well.
- Guardrails: permissions, approval steps, filters for sensitive data and audit logging.
AI agent vs. chatbot: the differences
The terms are often used interchangeably, but there are clear differences.
| Chatbot | AI agent | |
|---|---|---|
| What it does | Answers questions in a conversation | Carries out tasks to reach a goal |
| Input | A question from a user | A goal, an email, a document or an event |
| Output | A text answer | An action in your systems plus a report |
| Access to systems | Usually none or read-only | Controlled access through tools |
| Multi-step work | Limited | Plans and executes several steps |
| Control | Content of the answer | Permissions, approval steps and logging |
| Typical use | FAQ, first-line customer service | Order intake, planning, document processing |
Examples of AI agents in business
AI agents are most valuable where people now spend time on repetitive knowledge work between systems. Some examples:
- Order intake: the agent reads orders from emails and PDFs and creates them in the ERP, ready for a final check.
- Customer service: the agent answers questions about orders and delivery times by looking up the actual status in your systems.
- Planning support: the agent proposes a schedule based on capacity, priorities and changes, and the planner decides.
- Field service: technicians ask questions about manuals and procedures in natural language from their phone.
- Reporting: managers ask questions in plain language and get tables and summaries from the ERP.
When is an AI agent a good idea, and when not?
An AI agent is a good fit when the task is repetitive, involves unstructured input such as emails or documents, requires several systems and has a clear outcome that can be checked.
An agent is less suitable when every decision has major consequences and cannot be checked, when the necessary data is not available digitally, or when a simple rule-based automation does the job just as well. In that last case, a classic integration is often cheaper and more predictable.
Having an AI agent built: step by step
A successful AI agent starts small and is expanded based on results. A proven approach:
- Analysis: map the process and choose one task where AI adds clear value.
- Design: choose the model, tools, data sources and approval steps.
- Build and test: test with real examples, including the exceptions and edge cases.
- Integration: connect the agent to your ERP, CRM or other systems, with logging.
- Go live and improve: start with human approval, measure the results and expand step by step.
Risks of AI agents and how to manage them
Language models can make mistakes, and an agent that can act in your systems needs clear boundaries. The most important measures:
- Least privilege: the agent only gets the permissions it needs for its task.
- Human approval for actions with financial or legal consequences.
- Filtering of personal and sensitive data, and clear agreements about where data is processed.
- Logging of every step and tool call, so you can always trace what happened.
- Testing with realistic cases and monitoring after go-live, including the costs per task.