Agentic AI: what it is and how companies use it

Agentic AI is artificial intelligence that does not just answer but completes tasks: it plans the steps, uses your systems and finishes the work. It is built from AI agents connected to email, CRM or accounting that work with limited supervision and hand anything with consequences to a person for approval.

What agentic AI is

Agentic AI is the umbrella term for systems in which a language model is given not just a question but a goal. It decides which steps to take, which application to use and when the job is done. IBM, in its overview of agentic AI, describes it as AI that accomplishes a specific goal with limited supervision and consists of AI agents that each handle part of the work.

The basic building block is the AI agent. If you are new to it, start with What is an AI agent. Put simply, an agent is a language model with tools (reading email, finding an order, posting to accounting) and rules for when it may use them. Agentic AI means several such agents work together, pass work between them, and the whole process runs without anyone pushing it along by hand.

Agentic AI, chatbot or automation?

The three terms are often confused. The difference is who decides on the next step and whether the work actually gets finished.

	Chatbot	Classic automation	Agentic AI
What it does	Answers questions	Runs a fixed procedure	Plans and carries out the steps itself
Handles exceptions	No, hands over to a person	No, it fails or stops	Yes, within the rules
Works with systems	Mostly reads	Yes, by script	Yes, as the situation needs
Example	Answering a shipping question	Moving an invoice to a folder	Reads the invoice, matches it to the order, posts it and sends the difference for approval

In its engineering article Building effective agents, Anthropic distinguishes two kinds of agentic systems. A workflow guides the model along a predefined path; an agent chooses its own path. It also advises starting with the simplest solution that works, because an agent costs more and is slower than one well-tuned model call. We work the same way: where a fixed procedure is enough, we do not build an agent.

How agentic AI works in practice

Every agentic system has four parts that can be explained without technical detail:

• The model, the brain. It can be OpenAI's GPT, Anthropic's Claude, Google's Gemini or an open model running on your own server.
• Tools, the hands. Connections to email, CRM, your online store, accounting or a shared drive. The open Model Context Protocol (MCP) standard is now often used for this.
• Knowledge. Your policies, price lists, contracts and message history that the agent draws on, often a company AI knowledge base.
• Rules and oversight. What the agent may do alone, what a person must approve and what it may never do. Without this part, agentic AI does not belong in a company.

An example from an accounting firm: forty invoices arrive in the inbox in the morning. The agent reads them, extracts the data, checks company and VAT IDs, matches them to orders and prepares the postings. The accountant only gets a list of the five invoices where the amount does not match or the order is missing. That is exactly how our AI invoice processing works.

Where agentic AI saves companies the most work

Agentic AI makes sense where there is a lot of repetitive work that still needs some judgement. The most common areas:

1. Customer support. The agent answers on your website, by email and on WhatsApp, looks up the order and prepares the return. See autonomous customer support and the AI chatbot for e-commerce and Shoptet.
2. Email and back office. Sorting enquiries, answering routine questions, creating CRM records. More in process and email automation with AI.
3. Documents and orders. Invoices, delivery notes and B2B orders from PDFs or email.
4. Document-heavy industries. Real estate, law firms and online stores. See our pages for real estate agencies and law firms.

Twelve concrete examples are in What an AI agent can do for your business.

Risks and how to keep agentic AI under control

An agent that may act may also make mistakes. That is why every deployment has three layers of protection:

• Least privilege. The agent sees and changes only what the task needs.
• Human approval. Payments, refunds, sending a contract or deleting data are always confirmed by a person.
• Everything logged. Every step can be traced, so you know what the agent did and why.

We describe the full approach in AI guardrails: keeping agents under control. For companies in the EU the AI Act matters too; the European Commission summarises it on its regulatory framework for AI page. What it means for an ordinary company is in our guide The EU AI Act for companies: for example, customers must know they are talking to AI.

One agent or a multi-agent system?

There is a lot of talk about multi-agent systems, where several specialised agents share the work. One sorts incoming email, another handles orders, a third checks invoices and a fourth makes sure the others follow the rules. That makes sense for larger processes where one agent would have to know too much at once.

For most small and mid-sized companies, though, one agent for one process is the right start. It is cheaper, easier to tune and shows quickly whether it saves time. Add more agents once the first runs reliably. That is how agentic AI grows to cover a whole department without a big blind investment.

How to recognise a good agentic AI vendor

Many companies now offer “AI agents”, and some of them sell a chatbot under a new name. Before you choose, ask five questions:

• Will you show me the agent on my data? A good vendor builds a prototype on your own emails or documents, not a demo on a made-up example.
• What exactly may the agent do on its own? The answer should be specific: which systems, which actions, and what a person approves.
• Where will it run and where is the data? For sensitive data it is reasonable to require hosting in the EU or on your own server.
• What will it cost per month? Besides the build price, ask for an estimate of model and running costs.
• Who will fix it? Agents need tuning. Check that the team that built it will also run it.

If you want to compare options independently, start with an AI consultation and only then decide whether you need an agent or a better set-up chat is enough.

What agentic AI costs

The price has two parts. Building an agent for one process starts at €5,000 excl. VAT with us, and the agent is live within 30 days. On top of that you pay the AI provider for model usage; at normal volumes that is a small monthly amount we estimate upfront. All prices are on our pricing page, and a worked example is in How much does an AI chatbot cost.

How to start with agentic AI

Do not start with a strategy for the whole company. Start with one process that is frequent, dull and well described. A path that works:

1. Pick one process and count how many hours a week it takes.
2. Collect a few sample emails, documents or exports.
3. Have a prototype built and test it on real data.
4. Only when it works, connect it to live systems and train the team.

If you are not sure which process to pick, an AI consultation helps. If your team mostly uses chat so far, see how to roll out ChatGPT, Copilot and Claude in your company safely. Prahapp is the Prague division of Buinsoft Technology and builds custom AI agents with in-house engineers.

Try it without risk: within 3 working days we build a free, working agent prototype on your data. If you don’t like it, you don’t pay. Tell us which process costs you the most time.