How to build an AI agent for a company? Stages, data, and risks

Have you rolled out a chatbot and your first automations? Great – it’s time for the next step: an AI agent. Many companies make the same mistake, though: they start the project by choosing a tool instead of analyzing the process and the data. The result? A costly pilot that fails to deliver the expected results. An effective AI agent should have a clearly defined task, access to the right data, integrations with systems, and set boundaries for its actions. In this article, we explain what an AI agent is, how to plan its implementation step by step, how to determine the level of autonomy, what risks to take into account, and when such a project simply isn’t worth it.

An AI agent isn’t limited to answering questions. Its job is to carry out specific actions within a defined process. It draws on company data, instructions, tools, and systems it has been granted access to. It can prepare a summary, analyze a document, create a ticket, look up information, or plan next steps.

A well-designed AI agent operates within clearly defined boundaries. It doesn’t make arbitrary decisions but performs tasks according to established rules. We wrote more about how AI supports business processes in our article on AI applications in business.

A chatbot answers questions based on a conversation or knowledge base, so its role ends with communicating with the user. An AI assistant supports employees in everyday tasks such as writing, summarizing, or analyzing content, but a human still makes the decision and carries out the next actions.

An AI agent goes a step further. It can carry out an entire sequence of actions: check data, choose the right tool, trigger an action, and return a finished result. So the difference doesn’t come from the model being more “intelligent” – it comes from combining AI with the business process, tools, permissions, and control mechanisms.

An AI agent works best in processes that are repetitive, time-consuming, and based on working with data, documents, or systems. It’s a solution that fits wherever employees regularly perform similar tasks, e.g. checking information, copying data, preparing responses, or creating tickets.

A key condition for success is a clearly defined process owner and the ability to measure the results of the implementation. AI agents are most commonly used in HR, IT helpdesk, sales, finance, or document analysis. We wrote more about document work automation in a separate article.

The market clearly shows that this is the direction AI adoption is heading, although the adoption statistics themselves can be misleading. According to Gartner, in Q1 2026 as many as 80% of new enterprise applications already include an AI agent, compared with 33% in 2024. At the same time, analyses by Forrester and Anaconda show that as many as 88% of AI agent pilots never make it into a production environment. This is another argument for starting AI agent development with a well-defined process, and only then choosing the tools.

1. Choosing the process and the goal

It’s best to start building an AI agent with a single, clearly scoped task, rather than trying to create an “agent for everything.” At the start, it’s worth defining the business problem it’s meant to solve, identifying the user group, and the expected outcome – this could be an answer, a report, a recommendation, a ticket, or a record update.

Equally important is setting success metrics (KPIs), such as handling time, the number of cases handled, the number of errors, or user satisfaction levels. A helpful starting point is an AI audit, which will show which processes are actually suitable for this type of automation.

2. Preparing data and knowledge sources 

An AI agent is only as good as the data it uses. That’s why it’s worth first identifying knowledge sources – documents, procedures, regulations, CRM, ERP, and DMS systems, knowledge bases, or ticket history. The next step is to assess their quality, currency, and completeness, and to determine which data the agent may access.

Equally important is the ability to reference specific sources. This allows the AI agent to justify its answers, significantly reducing the risk of hallucinations and incorrect information. This is one of the most important – and at the same time most often overlooked – elements of the entire project.

3. Defining tools, integrations, and permissions

At this stage, you need to decide whether the agent should only answer questions or also perform actions in systems. It then needs to be connected to the tools used in the given process, such as CRM, ERP, helpdesk, email, DMS, or messaging platforms.

The foundation of security remains the principle of minimal permissions, aligned with the user’s role. Early in the project, it’s also worth defining which actions the AI agent can perform on its own and which require human approval.

Integrations are no less important. If the agent is to genuinely support employees, it should work smoothly with the systems they use every day, e.g. Microsoft Dynamics 365 Business Central. In a separate article, we’ve already shown how, in this environment, Copilot supports finance and sales processes.

4. Testing, piloting, and deployment

It’s worth testing the AI agent on real questions, documents, tickets, and unusual cases, not only on scenarios prepared in advance. After all, it needs to work in day-to-day operations, not only under controlled conditions.

Tests should cover not only the accuracy of the answers but also resilience to less obvious situations. At the same time, it’s worth collecting feedback from business users, who are best placed to judge whether the agent actually improves their work. Only on this basis is it worth refining the instructions, data sources, operating rules, and integrations before a wider rollout.

The level of autonomy of an AI agent should follow from the nature of the process and the risk involved. The lowest level means the agent only answers questions, offers suggestions, or prepares drafts, while all decisions remain with the human.

At the intermediate level, the agent can perform certain actions, but they require approval from the user. The highest level of autonomy assumes the independent execution of selected tasks, but only within a clearly defined and well-tested scope.

The greater the risk of the process, the higher the weight of the data, and the more serious the potential consequences of errors, the greater the human role in decision-making should be.

An AI agent, like any system that operates on data and performs specific actions, carries certain risks. These include incorrect answers and hallucinations, taking actions based on incomplete data, and access to confidential information, personal data, financial data, or information covered by trade secrets. Excessively broad permissions in company systems are an equally significant threat.

That’s why, already at the design stage, it’s worth ensuring action logging, auditability, quality control, and clear rules for escalating to a human.

It’s better not to start building an agent when a company has no clearly chosen process or defined business problem, and the data is scattered, outdated, low quality, or inaccessible. Expecting full autonomy without testing and oversight is risky – it’s a straightforward path to failure, as the statistics on failed pilots remind us. Sometimes less is more – simpler automation, a workflow, or an ordinary chatbot may be enough, and an AI agent would be overengineering relative to the actual need.

It’s worth assessing the effectiveness of an implementation based on specific indicators. A good starting point is comparing task completion time before and after deploying the AI agent, as well as the number of cases, tickets, or inquiries handled with its help.

Equally important are the number of errors, escalations, and cases requiring human intervention, as well as the level of adoption among users and its impact on costs and process quality. We described the methodology for measuring the results of AI implementations in more detail in a separate article.

Building an AI agent doesn’t start with choosing technology, but with understanding the business process. That’s why, at Euvic, we support companies from the analysis stage onward – we help select the processes that are genuinely worth automating, assess data quality, plan integrations, and identify potential risks even before the project begins.

We design AI agents that operate within clearly defined boundaries, with an appropriate level of security, control, and oversight. Instead of offering universal, “off-the-shelf” solutions, we create solutions tailored to specific processes and business goals.

If you’re wondering how to create an AI agent for a specific process in your organization, let’s talk about an audit, a PoC, and an implementation roadmap.