{"id":8513,"date":"2026-08-13T13:14:08","date_gmt":"2026-08-13T13:14:08","guid":{"rendered":"https:\/\/aihelp.lt\/?p=8513"},"modified":"2026-08-16T06:20:59","modified_gmt":"2026-08-16T06:20:59","slug":"ai-agentai-verslui-ka-verta-automatizuoti","status":"publish","type":"post","link":"https:\/\/aihelp.lt\/en\/2026\/08\/13\/ai-agentai-verslui-ka-verta-automatizuoti\/","title":{"rendered":"AI agents for business 2026: what is worth automating and when do they pay off?"},"content":{"rendered":"

Until recently, working with AI meant for many a simple action \u2013 to submit a request to ChatGPT or another AI tool and receive a response. In 2026, this logic is changing. More and more businesses are interested not only in individual AI tasks, but also Integrating AI agents into work processes<\/strong>, so that some actions are performed sequentially, automatically and with less human involvement. Google Cloud<\/a> identifies agent-based AI as one of the most important directions of business AI, and OpenAI and Microsoft are also actively developing agent-based solutions.<\/p>\n\n\n\n

However, an AI agent is not necessarily a better choice just because it is more advanced. For many tasks today, simpler solutions are still sufficient \u2013 ChatGPT, Gemini, Claude or another AI assistant. Therefore, before investing time and money in creating or implementing an agent, it is worth answering three questions first: what specific work an AI agent would take over in your business, how much time or cost it would actually save, and at which stages of the process human judgment and control are still necessary.<\/strong><\/p>\n\n\n\n

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What is an AI agent?<\/h2>\n\n\n\n

Google<\/a> An AI agent is defined as a software system that uses artificial intelligence to achieve goals and perform tasks set by users. AI agents can plan actions, use memory, make certain decisions, and act with a fair amount of autonomy.<\/p>\n\n\n\n

If an AI agent has the necessary access and tools, it can perform the entire workflow entrusted to it: collect information from different systems, evaluate it, make intermediate decisions according to established rules, prepare a response to the client and send it, update information in CRM or another system and initiate the next process step<\/strong>. A person can intervene in processes only at those stages where the risk of error is significant<\/strong>.<\/p>\n\n\n\n

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What is the difference between a robot, an AI assistant, and an AI agent?<\/h2>\n\n\n\n

Although the terms AI robot, assistant, and agent are often used synonymously, their capabilities, level of autonomy, and intended use differ. Google <\/a>provides clear differences between these solutions and practical examples of what tasks each of them can perform. <\/p>\n\n\n\n

System<\/th>What do they usually do?<\/th>Example<\/th><\/tr><\/thead>
Robot (bot)<\/strong><\/td>Responds to a question or set scenario<\/td>Answers the most frequently asked customer questions<\/td><\/tr>
AI assistant<\/strong><\/td>Helps a person complete a task<\/td>Prepares a text, analysis, or draft proposal<\/td><\/tr>
AI agent<\/strong><\/td>Is goal-oriented and can follow a multi-step process<\/td>Collects data, analyzes it, prepares a report and communicates the result<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

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It should be recognized that the boundaries between a robot, an assistant, and an agent are not rigid. A modern assistant can use tools, while a chatbot can have the capabilities of an agent. Therefore, when evaluating a specific solution, it is more important to look at:, how independently he can act and what actions he is allowed to perform<\/strong>. <\/p>\n\n\n\n

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What are AI agents like?<\/h2>\n\n\n\n

There is no single, universally accepted classification of AI agents. Different technology companies group agents according to different criteria: some \u2013 according to, what kind of work do they do<\/strong>, others \u2013 according to level of independence<\/strong>, and still others \u2013 according to system architecture and the way agents cooperate<\/strong>.<\/p>\n\n\n\n

How does Google classify AI agents?<\/h3>\n\n\n\n

Google Cloud<\/a> AI agents are classified according to their mode of operation. Interactive agents<\/strong> communicates directly with the person and responds to their requests, and background agents<\/strong> operates inside the system \u2013 automatically performs tasks, monitors events and initiates actions without constant human intervention. Systems can be classified according to the number of agents single agent<\/strong>, where a single agent performs a clearly defined task, or multiple agents<\/strong>, where several specialized agents collaborate to perform a more complex process.<\/p>\n\n\n\n

Google also distinguishes six broader categories of agent usage based on their practical business purpose.<\/p>\n\n\n\n

Google category<\/th>What can such an agent do?<\/th><\/tr><\/thead>
Customer agents<\/strong><\/td>Understand customer needs, answer questions, solve problems, recommend products or services, and operate across different customer service channels.<\/td><\/tr>
Employee agents<\/strong><\/td>Help employees complete repetitive tasks, find information, answer internal questions, and prepare, edit, or translate content.<\/td><\/tr>
Creative agents<\/strong><\/td>Generate texts, images and ideas, help with design, personalized content and marketing campaigns.<\/td><\/tr>
Data agents<\/strong><\/td>Analyze complex data, seek meaningful insights, and, depending on the system, initiate actions based on the analysis results.<\/td><\/tr>
Code agents<\/strong><\/td>Generate and analyze code, help fix bugs, work with codebases, and speed up software development.<\/td><\/tr>
Security agents<\/strong><\/td>Detect threats, help investigate incidents, and automate some of the prevention, detection, and response to security incidents.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

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It is important that this is grouping of agents by area of use<\/strong>, rather than technical agent types. Google also separately discusses finance, accounting, procurement, contract management, legal, HR, marketing, and multi-agent processes in its 2026 agent trends material.<\/p>\n\n\n\n

How does Microsoft classify agents?<\/h3>\n\n\n\n

Microsoft <\/a>emphasizes more the agent's level of autonomy<\/strong>. The simplest agents operate on the principle of "request-response", while more advanced ones can independently execute processes and respond to events without waiting for a human to submit a new request. Microsoft describes autonomous agents as systems that can monitor events, make decisions, and perform tasks according to specified triggers, instructions, and security rules.<\/p>\n\n\n\n

This can be thought of as a scale:<\/p>\n\n\n\n

The agent responds to a human query \u2192 the agent performs a specific task \u2192 the agent independently executes the entire workflow.<\/strong><\/p>\n\n\n\n

Therefore, two agents can be assigned to the same customer service area, but one will only prepare a response for the employee, while the other will check the order, respond to the customer, and update the information in the system.<\/p>\n\n\n\n

How does OpenAI classify agents?<\/h3>\n\n\n\n

OpenAI<\/a> in their practical guidelines for creating agents, they primarily classify them according to system architecture<\/strong> into single-agent and multi-agent systems.<\/p>\n\n\n\n

Single agent system<\/strong> uses a single agent that is given the necessary instructions and tools. OpenAI recommends that you first maximize the use of this model, as it is easier to manage and evaluate.<\/p>\n\n\n\n

When the process becomes more complex, it is possible to use multi-agent system<\/strong>, in which individual agents specialize in different tasks. OpenAI distinguishes two widely applicable models: a central "manager" agent that distributes work to specialists, and a decentralized model in which agents delegate tasks to each other.<\/p>\n\n\n\n

For example, a single customer service system may have a separate order agent, sales agent, and returns agent.<\/p>\n\n\n\n

There are also technical agent classifications<\/h3>\n\n\n\n

IBM also classifies agents according to their decision-making and learning mechanisms. They are divided into simple reactive, model-based, goal-oriented, utility-optimizing, and self-learning agents.<\/p>\n\n\n\n

This distinction is useful in a technical context, but for a business owner it is usually much more important to understand not what technical class the agent belongs to, but what business process agent<\/strong> can perform<\/strong>.<\/p>\n\n\n\n

Practical classification of AI agents for business<\/h2>\n\n\n\n

We recommend that you focus on business AI<\/strong> classification of agents according to the business function they perform<\/strong>. <\/p>\n\n\n\n

Purpose of an AI agent<\/th>What could he do in business?<\/th><\/tr><\/thead>
Customer Service Agent<\/strong><\/td>Respond to customers, check order information, change or register requests, resolve standard issues, and refer more complex cases to an employee.<\/td><\/tr>
Sales and e-commerce agent<\/strong><\/td>Process inquiries from potential customers, recommend products, check their availability, prepare offers, and assist in the sales process.<\/td><\/tr>
Employee and Insider Knowledge Agent<\/strong><\/td>Search for information in company documents, procedures, emails, or knowledge base and help employees complete daily tasks.<\/td><\/tr>
Administration and Process Agent<\/strong><\/td>Manage documents, register information in systems, sort requests, initiate workflows, and perform repetitive administrative actions.<\/td><\/tr>
Research and Analysis Agent<\/strong><\/td>Collect information from multiple sources, conduct research, compare data, look for changes, and prepare conclusions or reports.<\/td><\/tr>
Marketing and Content Agent<\/strong><\/td>Analyze the market, prepare content plans and drafts, analyze campaigns, adapt content for different channels, and execute repeatable content workflows.<\/td><\/tr>
Finance and Purchasing Agent<\/strong><\/td>Collect financial data, prepare reports, review documents, help with planning, and perform some of the purchasing or supplier processes. Google also identifies financial planning, accounting, and purchasing as early areas of use for agents in 2026.<\/td><\/tr>
Personnel agent<\/strong><\/td>Help administer employee processes, answer internal questions, or manage documents.<\/td><\/tr>
Programming and IT agent<\/strong><\/td>Write and debug code, test, analyze systems, help manage IT incidents, or automate technical workflows.<\/td><\/tr>
Security agent<\/strong><\/td>Monitor security signals, classify incidents, perform initial analysis, and initiate defined response actions.<\/td><\/tr>
Logistics and Supply Chain Agent<\/strong><\/td>Monitor inventory, deliveries, and supply disruptions, coordinate multiple process steps, and initiate actions based on defined conditions. Google provides an example in which an agent monitors the risk of spare parts shortages and can execute a backup supply plan within defined financial and logistical constraints.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

A single AI agent does not necessarily belong to just one category<\/h3>\n\n\n\n

In real business, these boundaries overlap. For example, an e-commerce agent can simultaneously be customer service, sales and administration agent<\/strong>. After receiving a customer question, he can check the order, find the remaining product, offer an alternative, send a response, and update the information in CRM.<\/p>\n\n\n\n

Therefore, when choosing an agent, it is better to start not with the question \u201e"What type of agent do we need?"\u201c<\/strong>, and from the specific process:<\/p>\n\n\n\n

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What does a person do manually in our business today, what steps in this process could be taken over by AI, and where should human control remain?<\/strong><\/p>\n<\/blockquote>\n\n\n\n

It is based on the answers to these questions that it is worth deciding, what kind of agent, what kind of access, and what level of autonomy is actually needed.<\/strong><\/p>\n\n\n\n

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Will AI agents replace workers?<\/h2>\n\n\n\n

Short answer \u2013 some tasks yes, but it cannot be concluded from this that an AI agent will completely replace a worker.<\/strong><\/p>\n\n\n\n

It would be more accurate to say that AI agents will not so much replace workers themselves as they will replace the composition of the work they do. Some repetitive, clearly defined tasks can be completely taken over by an agent, but the entire job usually consists of much more than one process.<\/p>\n\n\n\n

For example, a customer service representative doesn\u2019t just answer the most frequently asked questions. They handle non-standard situations, assess the customer\u2019s needs, coordinate decisions with colleagues, make exceptions, and take responsibility for the final result. An AI agent can independently answer typical queries, check order information, or perform other routine actions, freeing up the employee\u2019s time for more complex situations.<\/p>\n\n\n\n

As a result, deploying agents often involves more than a simple formula \u201e"humans are being replaced by AI"\u201c<\/strong>, and redistribution of work. The AI agent takes over more execution, and the human role shifts to decision-making, exception management, quality control, and accountability.<\/p>\n\n\n\n

Therefore, it is more beneficial for business to ask no \u201e"Can an AI agent replace a worker?"\u201c<\/strong>, and:<\/p>\n\n\n\n

\u201e"Which tasks for this employee can an agent perform faster or independently, and where is human knowledge, judgment, and responsibility still necessary?"\u201c<\/strong><\/p>\n\n\n\n

It is this approach that allows us to realistically assess where an AI agent can reduce manual labor<\/strong>, save time and increase employee productivity, and where automation would create more risks than benefits.<\/p>\n\n\n\n

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Does a small business need an AI agent yet?<\/h2>\n\n\n\n

Not necessarily. Sometimes a simple ChatGPT or other AI assistant and a well-described workflow solves the problem without any complex agent system.<\/p>\n\n\n\n

If an employee prepares one text once a week, a dedicated agent may not be necessary at all. But if the same process occurs every day, consists of several repetitive steps, requires information from multiple systems, and constantly takes up the employee's time, an agent may already have real business value.<\/p>\n\n\n\n

First, it is always necessary for a small business to assess whether an AI Agent is worth the cost. The benefits of agents should not be judged by how many actions they perform. It is necessary to assess, time saved, amount of human corrections, error rate, process cost and quality of the final result<\/strong>, and not just the fact that AI is used in the process.<\/p>\n\n\n\n

If you decide it's time to implement an AI agent into your business processes, we recommend starting with tasks that:<\/p>\n\n\n\n