What is an AI agent? (And what can it do for your business?)
A primer for non-technical decision-makers: what an AI agent is, how it differs from a chatbot or RAG system, and when it's the right — or wrong — tool for the job.
AI agent is the buzzword of 2026. Everyone has one — and almost nobody means the same thing when they say it. This article explains what an AI agent actually is, how it differs from a chatbot, RAG system or LLM, which patterns we see working in production, and — just as important — when you should not build one.
We write this as a software vendor that puts AI agents into production for mid-market companies. We've shipped agents that run marketing campaigns, classify legal documents, and analyse commercial leases. We've also said no plenty of times, because a simple workflow or an off-the-shelf SaaS tool was the better answer.
What is an AI agent, exactly?
An AI agent is a software program that takes a goal, plans its own approach, and autonomously executes the steps to reach that goal — usually by reasoning with a large language model (LLM) like Claude or GPT, and by calling tools (APIs, databases, other systems) along the way.
Three properties separate an agent from earlier AI applications:
- Goal-driven: you give it an objective ("process this incoming invoice"), not a script.
- Tool use: the agent decides which system to call to look something up or take action.
- Iterative: it can work step-by-step, evaluate intermediate results, and adjust its approach.
AI agent vs chatbot vs RAG: what's the difference?
These three terms get mashed together constantly, often by vendors who don't know the difference themselves. Here's a sober comparison:
| Chatbot | RAG system | AI agent | |
|---|---|---|---|
| Goal | Answer questions in text | Answer questions grounded in your documents | Complete a task autonomously |
| Has tools? | No | One tool: search documents | Yes — multiple APIs, databases, sub-agents |
| Steps | One question, one answer | One question, one answer (with context) | Many steps, with decisions along the way |
| Typical output | Text | Text with citations | An action in another system (ticket created, email sent, CRM updated) |
| Build time | 1–2 weeks | 3–6 weeks | 6–16 weeks |
RAG (Retrieval-Augmented Generation) is a technique, not a product. An agent can use RAG as one of its tools — that's what people call agentic RAG. A chatbot is usually the simplest of the three, and often all a business actually needs.
The four patterns we see in production
Not every agent looks the same. We typically work with these four patterns, in increasing order of complexity.
1. Single-purpose agent
Does one thing well. Example: our Document Classifier processes 50,000+ documents per month for a Dutch B2B services firm. One job — pull in an incoming PDF, classify it into one of 12 types, extract the right metadata, route it to the right system. No conversation, no UI. It runs, it works, it's cheap.
2. Multi-step workflow agent
Runs a chain of steps where each one can depend on the previous result. Example: our Marketing Automation Agent takes a short brief, does research, generates ad copy variants, places them into the right ad account, and monitors early performance. One client now ships roughly 10× as many campaigns with the same marketing team.
3. Agentic RAG
An agent that can search documents, but decides for itself what to query, whether to search again, and how to compose the answer. Example: our Lease Document Analyzer reads an 80-page commercial lease, decides which clauses to inspect, compares them against an internal legal standard, and produces a deviation report. A plain RAG bot can't do this — it can only respond to questions you explicitly ask.
4. Customer-facing agent
The most visible — and usually the hardest. An agent that talks to customers and acts on your business's behalf: scheduling, generating quotes, opening tickets in your CRM. Tone, error tolerance, and human escalation matter much more here. Don't start here — build internal agents first to learn where the risks live.
When you should NOT build an agent
This is the part most vendors skip. Agents are slower, more expensive, and less predictable than ordinary software. In three situations they're simply the wrong choice:
- When a deterministic system would do. An agent that always has to do the same thing is an expensive way to build a simple thing. An n8n workflow costs a fraction and fails more predictably.
- When you don't have data. Agents work on context. No good historical data, no documented process, no examples of what "good" looks like — even the smartest agent will produce noise.
- When compliance forbids it. In regulated scenarios (medical diagnosis, credit decisions, legal advice to end-customers) the EU AI Act or sector rules forbid autonomous AI decisions. A human must stay in the loop — and an agent is often not the most efficient form there.
Scoping an agent project: five questions
Before we build an agent, we work through these five questions with the client. If any answer stays vague, we don't build — or we start with a two-week discovery to sharpen them.
- What decision or action does the agent ultimately take? Not "it should help with X" — literally what output do you expect.
- What is the most common happy path, and what are the three most likely failure modes we should design for?
- Which systems must the agent read from and write to? CRM, ERP, mailbox, document store?
- Who gets the case when the agent is unsure or stuck? One person, a team, a queue?
- How will we measure that it's working? (More on this below.)
How do you evaluate an agent in production?
An agent isn't classical software where tests pass or fail. It makes probabilistic decisions. So you evaluate it differently:
- An evaluation set of 50–200 examples of real inputs + the desired outcome. For every release we run the agent across the set and check the score.
- Production monitoring: each run logs steps, tool calls, intermediate outputs and the final answer. We review outliers and failures weekly.
- Human review on a 5–10% sample of runs in the first months. It's not fun, but it's how you find real failures your eval set missed.
- Clear escalation rules: when does the agent itself raise a hand and say "I can't do this"?
Frequently asked questions
Do I need my own LLM?
Almost never. For 95% of use cases an API call to Claude or GPT works fine. Training or fine-tuning your own LLM costs hundreds of thousands of euros and only matters if you have genuinely unique data or strict compliance constraints.
Will an agent replace my staff?
Sometimes a part of the job, rarely a whole role. The most successful agents in 2026 take over the boring repetitive part so people can focus on exceptions and judgement calls. Plan for that — not for layoffs.
What does it cost to build an AI agent?
A simple single-purpose agent: €15K–€40K. A multi-step workflow agent with integrations: €40K–€120K. A production-ready customer-facing agent: €80K–€250K. Plus 15–25% per year for maintenance, because models change, prompts break, and your process evolves.
How long does it take?
Our typical agent projects run 6 to 16 weeks from discovery to production. Shorter usually means too rushed; longer usually means the scope was too broad.
How we build agents
We build AI agents for mid-market companies. We almost always start with a 2–3 week discovery to sharpen the five questions above, set up an evaluation set, and show a working prototype. Only after that do we move to a fixed-price build. More about our approach is on our AI agents service page.
Want to talk through a concrete use case? Describe the process you want to automate in a few sentences via our contact form — we'll respond within one working day with either a ballpark price or an invitation to dig deeper.