What Is an AI Agent and How Does It Work

What Is an AI Agent?#

An AI agent is a software solution built around a language model that independently perceives the state of a task, decides what the next step should be, and carries out that action through connected systems, without waiting for an employee's instruction at every single step. Unlike an ordinary chat assistant, an agent doesn't just answer questions - it acts: it reads a document, checks a value in another system, creates a record, or triggers the next step of a process. In a business setting that means a tool that takes over part of the operational work, not just an advisory role.

The core of an agent consists of three parts: a language model for understanding and reasoning, access to data (documents, databases, ERP or CRM records), and a set of actions it is permitted to carry out through APIs. Without the third part, it's merely an assistant that offers advice; it is the ability to execute an action - entering data, sending a notification, changing a status in a system - that separates an agent from a classic chat interface. That's why, in a business context, the question of what an AI agent is should be paired with the question of which actions it may take on its own and where its authority ends.

Agents differ in how much autonomy they have. Some carry out a single, narrowly defined step - for instance extracting a value from an invoice and entering it into a system - while others chain decisions across several systems at once: checking stock, preparing an order, notifying the responsible person, and logging the outcome. For a business decision-maker, this distinction matters more than the model's technical details, because it directly determines how much control must be retained, where human confirmation is needed, and who is accountable if the agent makes a wrong call.

How Does an AI Agent Work in a Business Setting?#

An AI agent works through a perceive-reason-act-verify loop: it reads an input or trigger, the model decides what needs to happen, the agent carries out the action through a connected system, and then checks the outcome before continuing or closing the task. This loop can repeat several times within a single task until the agent reaches a state that matches the stated goal. Unlike a classic program that follows pre-written rules, an agent assembles its path to the goal on the fly, based on the content of the input.

In practice the loop starts with a trigger: an incoming email, a new record in a system, an uploaded document, or a schedule. The agent then reads, through an API, whatever additional data it needs - order status in the ERP, customer data in the CRM, or the content of a document in the document management system. Based on that data the model infers the sensible next step and translates the decision into a concrete system call: entering a record, changing a status, sending a notification, or preparing a summary for an employee.

A key part of how it works is a control layer: rules on which actions the agent may take on its own and which must wait for human confirmation before execution. For low-risk tasks, such as sorting documents or drafting a report, the agent's autonomy can be high. For tasks affecting payments, contracts, or personal data, it makes sense to always keep an intermediate confirmation step. That boundary isn't a technical detail - it's a leadership decision that must be set before rollout, not during it.

The agent's behaviour is also visible from the outside, through an event log: which data it read, which decision it made, and which action it executed. That trail is needed for audits, troubleshooting, and accountability - if the agent triggers a wrong action, it must be clear which data and which logic led to it. Without such a log, the agent becomes a black box for the company, which is not acceptable in a business environment where decisions have to be justified.

How an AI Agent Differs from Classic Automation and a Chatbot#

Classic automation executes a pre-written procedure: if condition A is met, step B follows. It works reliably as long as the input matches the expected shape, but fails when an exception appears that the rule's author didn't anticipate. Instead of a fixed rule, an AI agent uses reasoning: from unstructured input - free text, a scanned document, speech - it extracts meaning and then chooses the appropriate next step itself. That's exactly why an agent is useful precisely where classic automation fails - with exceptions, incomplete data, and cases that differ from one another.

A chatbot answers questions within a conversation, but on its own it doesn't change the state of other systems. An AI agent goes a step further: it translates the answer it forms into an action - entering data, triggering a process, or notifying the responsible person. The difference isn't cosmetic but organisational: a chatbot needs an employee to read the answer and act on it, while an agent takes over that work instead. For a company that means a different risk calculation, since the agent acts directly on production data, not just inside a conversation window.

In practice, solutions are often combined: part of the process stays classic automation - checking formal conditions, triggering schedules - part is taken over by an agent - understanding content, judging exceptions, preparing a decision - and part stays with an employee - final confirmation on sensitive steps. Leadership planning such a project therefore needs to draw these boundaries in advance, otherwise the project grows beyond a scope that can still be reliably tested and maintained.

Where AI Agents Are Used in Business#

AI agents in business are most often deployed where there is a lot of repetitive work involving unstructured data - documents, email, free text - and where each case differs slightly from the last. A typical example is processing incoming invoices, contracts, or applications: the agent reads the document, extracts the data, checks it against an existing record, and prepares a proposal for further processing. The same applies to customer support, where the agent classifies an inquiry, finds a relevant answer, and, where needed, triggers the next step in an internal system.

• Document processing and data extraction (invoices, contracts, applications, forms)
• Customer support and internal employee support - triage, answers, escalation
• Preparing reports and summaries from multiple data sources
• Checking and reconciling data across systems (ERP, CRM, document systems)
• Monitoring processes and automatically notifying responsible staff
• Semantic search across internal documents and knowledge bases

Which Systems Need to Be Connected for an Agent to Work#

An AI agent only becomes useful once it has access to data and actions inside a company's existing systems - without that connection it stays limited to general conversation with no insight into the actual state of the business. In practice this means connecting to an ERP system for order, stock, or invoice data, to a CRM system for customer data, and to a document management system for contracts, applications, or internal documentation. Companies commonly rely on one of the established systems for these purposes, such as Pantheon, Microsoft Business Central, or SAP on the ERP side, or Salesforce and HubSpot on the CRM side.

Each of these systems has its own API, its own data structure, and its own access rules, so connecting to it isn't a one-off step but a project in its own right: you need to determine which data is actually needed, in what format the system returns it, how often it changes, and who is allowed to access it. Where an existing system has no public API, or an outdated one, that layer has to be built first, before the agent can even be connected to it.

Besides ERP and CRM systems, reporting tools and document systems - for example Microsoft 365, SharePoint, or Power BI - often need to be connected as well, whenever the agent prepares summaries or reports from several sources at once. The complexity of a project therefore depends not just on what the agent is meant to do, but above all on how many systems it needs to connect to and how demanding each of those connections is. That assessment is the first step of any serious implementation planning.

Risks and Accountability When Deploying AI Agents#

The biggest risk with an AI agent isn't a technical error in the model, but an unclear boundary around its autonomy: who is accountable if the agent carries out an action that was wrong or inappropriate. That's why, before rollout, it has to be decided which actions the agent may take on its own, which require human confirmation, and who inside the company owns the responsibility for overseeing how it behaves. Without that agreement, accountability after launch often stays unclear between departments, which slows down the response when something goes wrong.

A second risk is the quality of the data the agent reasons over. If data in the ERP or CRM system is incomplete or outdated, the agent makes a decision on a flawed basis, regardless of how good the model itself is. Implementing an agent therefore often surfaces data problems that already existed in the company beforehand but went unnoticed as long as a human was making the decisions and intuitively correcting for the gaps.

A third risk concerns the protection of personal and commercially sensitive data. An agent that reads documents, email, or customer records comes into contact with personal data in the GDPR sense, so it must be decided in advance which data may be part of the model's input, how long records of its activity are retained, and who has access to the log of its decisions. In regulated industries and the public sector, this question matters just as much as the question of what the agent actually does.

How Do You Keep Data Secure with an AI Agent?#

Data security with an AI agent comes from separating environments, controlling access to systems, and tracing every decision - not from trusting that the model will figure out on its own what it may and may not do. The agent must only have access to the data and actions it actually needs for its task, not to the entire system it's connected to. That access restriction is the first and most important security measure, because it limits the damage even when a reasoning error occurs.

Beyond technical measures, an organisational agreement is also needed: who reviews the agent's activity log, how often, and what happens if the agent repeatedly makes borderline decisions that need checking. Securing an AI agent isn't a one-off review at launch but an ongoing process, similar to maintaining any other production system - except that the agent makes decisions, so a flaw in its logic can go unnoticed longer than a bug in a classic program if nobody reviews the log regularly.

• Separate development, test, and production environments, with no real personal data in the test environment
• Agent access to data and actions limited to what the task actually requires
• A log of all the agent's decisions and actions for audit and troubleshooting
• Clearly defined actions that require human confirmation before execution
• Regular security reviews of the connections between the agent and existing systems
• Compliance with GDPR and internal data-retention rules

How Do You Choose a Partner to Implement an AI Agent?#

It makes sense to choose a partner for implementing an AI agent based on whether they can cover the whole scope of the project - from analysing existing systems, through architecture and development, to security, testing, and post-launch support - not just building the model or the interface. An agent that operates in isolation from a company's existing systems has little practical value, so connecting it to ERP, CRM, and document systems is often the more demanding part of the project compared with the AI piece itself. The partner needs to be able to handle both at once.

For larger projects it's worth checking how the partner organises the work as well: whether they assign a project lead, how communication and reporting flow between phases, and how the scope is broken into parts that can be tested and accepted separately. This matters especially when the agent touches several departments at once, since an unclear split of responsibility between the partner and the client often causes delays that stem from poor project organisation rather than technical difficulty.

The same applies to the period after launch: a partner who can take over monitoring, bug fixing, and further development of the agent reduces the risk that the solution is left without maintenance after the first few months. Because the business systems the agent connects to change over time - new API versions, altered data structures - the agent needs regular technical oversight, similar to any other production system in the company.

• Experience connecting AI solutions to existing business systems through APIs
• A clearly defined testing process before release to the production environment
• A defined level of post-launch support and responsiveness (SLA)
• An understanding of data-security and GDPR requirements in your industry
• The ability to take on the project in full or as individual work packages
• Documentation, access credentials, and source code that belong to the client at handover

How AI Agent Implementation Unfolds Inside a Company#

Implementing an AI agent starts with analysis: which process the agent should take over, which data it needs, which systems it has to connect to, and where human confirmation must be kept. This phase also surfaces any gaps in existing data or connections that need fixing before development even begins. Without this analysis, a project often grows beyond its original scope, because the need to connect additional systems only comes to light during development.

Architecture and development follow: deciding which part of the logic the model handles, which part stays classic code, how data access is set up, and where human confirmation is built into the process. Testing runs in parallel - manual and automated, including edge cases where the agent must not make an autonomous decision but has to hand the task to an employee. The test environment stays separate from production and contains no real customer personal data.

After release into the production environment comes a monitoring period, during which it's checked whether the agent's decisions match expectations, with its logic or the scope of its autonomy adjusted as needed. A change identified during monitoring goes through review and testing like any other system change before it takes effect in production. This phase is often overlooked in budget and timeline planning, even though it matters as much as the development itself.

In the long run, an agent needs the same level of maintenance as any other production system: technical and security updates, monitoring, and a response whenever a disruption or outage occurs. The severity class of an issue - from a full outage to a minor bug with no business impact - determines how quickly it must be addressed and whether the fix is scheduled for the next release. Leadership that plans for this in advance avoids a situation where the agent operates without a clear owner inside the organisation.

What Deploying an AI Agent Means for Leadership#

For a company's leadership, deploying an AI agent isn't primarily a technology decision but an organisational one: which part of the work it is prepared to hand over to a system, where human confirmation has to stay, and who inside the company takes ownership of overseeing it. These questions need to be settled before the project starts, not during it, since they determine the scope of system connections, the level of security measures, and the testing approach.

The same applies to the impact on employees: an agent that takes over repetitive work changes the content of the jobs held by the people who previously did that work, so it's worth planning in advance how their role shifts - toward overseeing the agent, reviewing exceptions, or work the agent doesn't cover. A project that addresses this question only after launch often meets resistance that could have been avoided by involving employees already during the analysis phase.

The decision to deploy an AI agent should therefore be treated like any other project that touches several departments and existing systems at once: with a clear scope analysis, defined oversight responsibility, and an agreed level of post-launch support. That approach reduces the risk of the solution being left without an owner after the first few months, and increases the odds that the agent actually relieves the work it was designed for.