What AI agents are, why businesses need them, and how to implement them in IT infrastructure with risks and benefits in mind

Gartner predicts that by 2028, AI agents will make up to 15% of daily work decisions without human involvement. Already, 72% of companies are implementing AI solutions, but they often confuse the terms - where chatbots end and fully autonomous agents begin. We explain what AI agents are, what functions they perform, how to choose the right solution, and how to integrate it into workflows with security in mind. We also review real cases from different industries and provide practical recommendations.

AI agent - an autonomous digital employee powered by artificial intelligence that _reads data, makes decisions, and executes multi-step business processes without constant human involvement_.

Such systems became possible thanks to the development of large language models capable of _not only generating text but also acting in a digital environment._ Unlike scripts and chatbots, an agent works with unstructured data, adapts to changes, and learns from its own experience. The agent works like this: receives a signal → thinks about what needs to be done → does it. It can pull data from CRM, email, or a BI system, create an action plan, and carry it out by interacting with the necessary services.

For example, if a regular assistant produces a quarterly report template, an AI agent will gather the figures, identify trends, format the slides, and send the finished presentation to the manager.

How an AI agent works - Large language models (LLM) - the agent's "intelligence": understands requests and builds the logic for actions. - Tools and API - communication channels with external systems: CRM, email, databases, messengers. - Memory - the agent remembers previous steps and uses context so it does not start from scratch each time. - Planning and reasoning - breaks the goal into steps, evaluates options, and chooses the best path.

Why businesses need AI agents Agents are changing how companies work with data and tasks. They automate _complex processes where a simple algorithm is not enough,_ and deliver tangible value: 1. Reduce time and costs for complex tasks- instead of dozens of manual actions, the system combines several steps into one flow. In insurance, it can gather documents, verify them, assess risk, and issue a ready policy in minutes - work that takes a person hours.

2. Improve service quality and response speed - the agent works 24/7 and for each customer can do more than answer: it can track an order, predict a delay, find the best option with partners, and offer compensation, preserving loyalty. 3. Eliminate bottlenecks and human errors- in finance, the agent performs initial borrower scoring, checks dozens of parameters in seconds, and delivers a justified decision, reducing analyst workload and lowering risk.

4. Accelerate strategic decisions - instead of spending weeks preparing reports, the sales director receives a weekly digest from the agent showing what is growing, what is declining, and where efforts should be redirected.

In real estate, the system analyzes rates, occupancy, and market trends to suggest the optimal price and tenant retention strategy. Example:the company "SberAutoSubscription" implemented an AI agent that processes requests on its own, communicates with customers in messaging apps, selects cars, schedules test drives, and sends visit reminders. As a result, conversion increased by 25%, and operating costs for lead management dropped significantly.

As noted above, AI agents _improve business processes, from analytics to autonomous transactions._ Understanding which systems exist and when to use each one helps you choose a solution that will truly deliver results. How AI agents evolve An agent is not a single technology, but a spectrum of systems that differ in autonomy and intelligence. The table below shows the main types and use cases.

How to choose the right AI agent for business Choosing the right AI agent is a practical decision based on your tasks, process maturity, and expected return. Below are 3 steps to help determine which agent is right for you. 1. Start with a task. Define what role the agent should play: simply react, plan actions, or learn from experience. For example, a reactive agent is suitable for collecting feedback, while a learning agent is better for predicting customer churn.

2. Check process readiness. The system must have access to data and applications (CRM, ERP, BI). If processes are not digitized, start by formalizing them, not with AI. 3. Compare benefit and complexity. The most complex solution is not always the most profitable. Often, a goal-oriented agent with scenarios is enough. Complex multi-agent systems make sense only when automation delivers a high return.

According to McKinsey, 62% organizations are already testing AI agents, and 23% are moving on to scaling solutions. The potential is high - according to research, generative AI can add from 2.6 to 4.4 trillion dollars added to global GDP each year.

To use these capabilities in business, it is important to approach implementation with a clear plan. 1. Determine exactly what to automate First, define _what task the agent should solve._ It may simply react to events, plan actions, learn from experience, or do all of these. Focus on processes where manual work is expensive or where frequent errors slow things down. For example, analyzing incoming requests and preparing responses is an excellent area for automation.

But simple FAQ bots that only repeat predefined answers usually do not require an AI agent - simpler systems can handle that. 2. Prepare the data and connect systems An AI agent needs _clear data and access to information sources._ Clean up the data in CRM, ERP, BI systems, and other repositories, and make sure it is accurate, consistent, and structured.

Set up access through API so the agent can read data and then send the results to where they will be used (for example, to employee dashboards or messengers). If your processes are not yet digitized, do that first - an agent cannot help where there is no digital trail.

3. Build a prototype before creating the "final product" At the development stage, decide how you will build the agent. Often, _low-code/no-code platforms_ make it possible to create working prototypes in days or weeks without spending months on manual programming - this is useful for quickly validating an idea. If the task is unique and requires fine tuning, you can bring in specialists and build the solution on top of an SDK or cloud AI platforms.

Start the pilot on a _narrow scope_ first: one department, one type of task, or a limited data flow. The goal is to check whether the agent works as expected. 4. Evaluate the result and refine it Evaluate the pilot not by the number of features, but by concrete outcomes: how much faster tasks are completed, how accuracy changes, how much human intervention is needed, and how employees respond to the agent. Collect feedback and improve the logic - that is a normal part of the process.

Often this means revising prompts, optimizing scenarios, or configuring integrations. 5. Scale and expand use cases If the pilot has proven its value, you can _expand the agent's use._ Start with related tasks or departments: if it handled emails well, add work with CRM requests or notifications for managers. It is important to ensure infrastructure that can handle growing load and to keep measuring effectiveness.

Security is mandatory When connecting an agent to APIs and data, you open up new access zones. Be sure to conductsecurity audit: control what actions the agent can perform, limit access rights, monitor request rates, and watch how sensitive data is processed. The stability of the entire system depends on this directly.

When you deploy an AI agent, you are responsible for its actions. In CIS, the rules for AI are still evolving: in addition to _Federal Law 152__on personal data, there is the _"Concept for Regulating AI and Robotics,"_ and a separate AI law is also being prepared _(draft No. 189343-8)_. We will break down the key points to consider so you do not face fines or customer claims. 1.

Transparency of decisions An AI agent can make hundreds of decisions a day - but you must understand _how and why it makes them._ This is especially important in lending, hiring, or healthcare, where a person's future may depend on the decision. The law requires that such decisions be explainable.

What to do: - Log the agent's actions - keep the steps, input, and output. - Use Explainable AI methods to see which factors influence decisions. - A human must always review critical decisions (loan approval, launching a marketing campaign, releasing a new product) - document this in your policies. 2.

Data security and privacy The agent works with a large amount of information, including personal data. This means you _must strictly comply with Federal Law 152_ - collect only the data you need, obtain explicit user consent, and anonymize information where possible: - Monitor what data the agent sends to cloud models through API.

Personal data cannot be sent there without proper protection. - Implement automatic filters or proxies that remove sensitive data from requests before they are passed to external services. 3. Algorithmic bias If an agent is trained on old data, it may "inherit" errors and prejudices. For example, a hiring system that "sees" discrimination in past practice will repeat it.

How to avoid it: - Regularly check the agent's outputs with statistical tests. - Compare results in A/B tests. - Adjust training data to remove bias. Keep in mind:problems can arise not only from explicit discrimination, but also from indirect signals that may seem neutral at first glance but in fact point to a person's social status.

For example, if an agent uses postal code, it can unconsciously influence the decision, since it is often linked to income level or neighborhood. 4. New standards and requirements A new AI law may introduce the concept of "trusted AI" and strict requirements for high-risk systems. This means such agents may need: - certification; - built-in safety mechanisms; - liability insurance.

You should already assess your vendors and platforms for readiness to meet these requirements. Where tasks are critical to the business, it is useful to duplicate functions and eliminate a "single point of failure".