AI agents, RPA and assistants

Employees spend 60% of their working time on repetitive tasks: data collection, information transfer, and approvals. These tasks slow growth and reduce team efficiency. The solution is AI agents, which take on complex processes end to end: they plan steps, use digital tools, and deliver a finished business result without human involvement. Employees spend up to 60% of their time on routine work - AI agents replace entire business processes, reducing costs and speeding up task completion.

Unlike RPA and assistants, AI agents work autonomously: they plan steps themselves, use APIs, and achieve results without human involvement. Agent-based automation is used in sales, analytics, HR, logistics, finance, and manufacturing - from recruiting to demand forecasting. CIS cases (Sber, StroyGrad) show conversion growth of up to 18%, a reduction in staff workload of up to 35%, and up to 70% of requests processed without human involvement.

Implementing an AI agent requires preparation: process audits, clean data, a pilot team, and clear KPIs - from reducing AHT to increasing CSAT and saving budget. We explain what an AI agent does, in which scenarios it is more effective than RPA and assistants, what tasks it solves, and how to measure the real value of implementation.

What an AI agent is and how it works

An AI agent is a system based on large language models (LLMs) that solves multi-step tasks on its own. If an AI assistant helps a person by searching for information or generating text, an agent acts independently. It receives a general task, breaks it into steps, uses the required tools, and delivers a finished result - a report, calculation, segmentation, or request. AI agents differ from other solutions because they operate autonomously and flexibly.

Unlike RPA systems, which strictly repeat predefined actions in the interface, the agent chooses how to solve the task on its own. Traditional automation stops at any error or deviation from the scenario. The agent does not: it analyzes the situation, adapts to changes, and suggests another path if the first one does not work. How an agent works internally: Planning. The agent breaks your goal into steps on its own. You say: "analyze customer feedback."

It creates a plan: collect data from CRM and social media -> determine the sentiment of reviews -> identify the main themes of complaints and praise -> prepare the final report. Tools. The agent actively uses your business systems. It works through API: searches for information online, requests data from a database, sends emails, creates tasks in Trello or Jira, generates reports, and stores them in the corporate data warehouse. Memory. The agent remembers what it has already done.

It preserves the context of the entire workflow and uses the results of previous steps for subsequent decisions. This helps carry out complex actions in a deliberate and coherent way. Execution and feedback. The system checks whether the step worked, and if not, tries another option: it changes the data source or the processing method. Comparison table: AI agent vs AI assistant vs RPA.

Criterion	AI Assistant	RPA bot	AI Agent
Autonomy	Waits for every request. You stay in control.	Blind rule-based automation.	Plans and executes a multi-step task on its own.
Result	Provides information, text, and ideas.	Transfers data from one system to another.	Executes a business process and delivers a finished result (a request, a completed transaction).
Flexibility	Works in conversation.	Stops at the slightest deviation from the script.	Analyzes context, looks for workarounds.
Task Example	"Write a sample proposal for an IT company."	"Transfer 100 rows from Excel to CRM."	"Analyze the lead database for the month, segment it by industry and company size, draft personalized proposals, and send them out."

An AI agent is an autonomous executor that creates a plan for a task and carries it out using your digital tools. It replaces not a single click, but an entire business process.

What an AI agent does: 6 key functions in business

According to IBM, by the end of 2025 the share of processes performed by AI agents will grow from 3% to 25%. That means every fourth business process will be carried out without human involvement. Below are the main areas where an AI agent shows the greatest effectiveness.

1. Sales automation and customer service

The system guides the customer end to end, from the first contact to closing the deal. It processes incoming requests, clarifies the details of the request and checks stock availability in the warehouse in real time. Example: a customer places an order for 100 items. The AI agent checks the stock, applies a discount, generates an invoice and sends a reminder after 2 days if payment has not arrived.

2. Analytics and reporting

The AI agent collects and analyzes data from the company's various systems on its own. It combines information from CRM, analytics systems, and internal databases, then consolidates it into ready-made tables and charts so the manager immediately sees where revenue dropped or orders fell. Example: in 5 minutes the system prepares a revenue report by branch, showing the three leading regions and the reasons for the sales decline in one of them.

3. HR and internal processes

In the HR department, the agent automates initial candidate screening by checking resumes against basic requirements. The system arranges interview dates with applicants on its own, working through the available slots in the calendar. For new employees, the agent answers standard questions about workflows and documents. Example: the agent reviews 200 resumes, selects 10 suitable candidates, and schedules interview times with them.

4. Logistics and procurement

The AI agent forecasts product demand by analyzing historical data, seasonality, and market trends. It adjusts procurement and production plans when demand changes, tracks the movement of goods in real time, and warns about possible delays. Example: the agent analyzes demand, checks current supplies, and finds an additional supplier to avoid a shortage.

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5. Financial forecasting and control

The system shows whether there is enough money for purchases, how much will come in from customers, and warns about a possible cash gap. It accounts for seasonal fluctuations, sales plans and the market situation: if actual expenses exceed the plan, the system reports this and suggests where costs can be cut. Example: before the New Year, the agent forecasts a sales spike, advises postponing part of the costs and shows the risks of a working-capital shortage.

6. Manufacturing and equipment maintenance

In manufacturing, an AI agent monitors equipment via IoT systems.
It analyzes sensor data and predicts possible breakdowns before they happen.
The system generates maintenance requests at the right time.
If one section is overloaded, the agent moves orders to another one - without dispatcher involvement. Example: the agent detects overload on one section, initiates maintenance, and moves part of the orders to another line so time is not lost. An AI agent helps simplify processes and reduce the team's workload - this is real savings in time and resources.

1. Sber - deploying an autonomous agent in the contact center

Problem: Sber's contact center handled millions of calls every day. Because of routine tasks (balance checks, card blocking, account information), operators had no time to resolve non-standard customer requests that required empathy and deep analysis. Solution: the bank deployed an AI agent to automate the handling of incoming calls. The agent does not simply repeat memorized phrases — it understands the context of the conversation, asks clarifying questions and independently resolves up to 30% of typical requests (account information or service activation).

Implementation results: - Inquiry automation - voice and text bots independently resolve about 70% of all customer questions. - Call processing speed - average call routing time for corporate clients has been reduced 3.5 times to 18 seconds. - Service quality - the virtual assistant satisfaction index (CSI) reaches 82%, which is at the level of a human operator. - System performance - the system processes

up to 19 million calls per month, and on peak days it handles 70% of pension arrival calls on its own. - Industry recognition - according to a Frank RG study, Sber's contact center was recognized as the best in CIS and received awards for the best robotic service.

2. StroyGrad developer - deploying an AI agent to automate sales

Problem: a major real estate developer faced a heavy workload on its sales managers. Customers reached out through the website and social media after hours, but their requests went unanswered until morning. Managers spent 60% of their time answering typical questions about apartment sizes, finishing and promotions. This led to the loss of up to 40% of potential customers at the first-contact stage. Solution: a team of experts deployed an AI agent for real estate development that works 24/7.

The system was trained on a database of all the company's properties, mortgage terms from partner banks and current promotions.

The agent performs the following functions: - Advises clients on the apartment catalog, taking into account their budget and preferences. - Estimates mortgage payments for different banks. - Informs them about current promotions and early-booking discounts. - Answers common questions about the handover date, finishes, and documents. - Identifies interested clients and passes their contact details, along with the full conversation history, to managers in the CRM system.

Implementation results: - Customer response time decreased from several hours to 10 seconds. - The number of requests processed outside business hours increased by 65%. - The share of qualified leads in the total flow increased from 20% to 45%. - Booking conversion increased by 18% thanks to prompt contact. - The workload on sales managers decreased by 35%, allowing them to focus on hot leads.

Both cases show how AI agents solve a key business problem - they automate multi-step processes rather than isolated operations. As a result, companies process more requests without increasing headcount, reduce service time, and improve conversion, while freeing employees to handle complex tasks.

Preparing your company to deploy AI agents: a checklist for managers

For an AI agent to truly help the business, proper preparation is essential. This reduces risk and helps you get results faster - in time savings, cost savings, or revenue growth. Implementation checklist by stage:
Conduct a process audit. Identify repetitive tasks in employees' work that can be automated. The ideal "candidate" is a process where a specialist spends more than 30% of their time on routine work and number handling.
Assess data quality. Make sure the information in your databases is complete and up to date.

The agent learns and operates on your data, so errors in it will lead to wrong decisions.
Form a pilot team. Assign a manager who knows the business process as the owner, and bring in a technical specialist. This ensures both the business logic and the technical implementation.
Choose a reliable vendor. Look for companies with proven cases in your industry. Ask how they ensure the agent's stability and whether they provide technical support after launch. 5.

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Launch a pilot project. Choose one non-critical but representative process for testing. This will let you assess the real impact and refine the implementation method with minimal risk. 6. Set up system monitoring and improvement. Regularly track the agent's key performance indicators after deployment. You will see where the agent makes mistakes or slows down and can adjust the algorithm immediately - without reworking the whole system. Tip: do not try to automate a complex process with ten steps right away.

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Instead of automating the entire sales cycle, automate one task - for example, collecting feedback after a deal. This will show quick results: feedback is collected in 15 minutes instead of 3 hours. The team will see the value, and it will be easier to move on to more complex processes. A step-by-step AI agent implementation plan will help you reduce risks and get the first measurable result within a few weeks.

Key success metrics

Before starting a project, it is important to define the metrics (KPIs) by which success will be measured.
Tie the agent's work to profit - calculate how much money it saves or earns, not how complex its architecture is.
Reduction in AHT (Average Handling Time): the time spent on one request is a direct measure of efficiency.
Agents can reduce AHT by 40%.
FTE Reduction: automating 80-90% of routine requests allows companies to reallocate or cut headcount in contact centers and IT support.
This is the key savings metric, which can reach 5 million ₽/year on IT support alone.
Indirect profit: faster response times in retail chains or banks raise satisfaction, which converts into direct revenue. For instance, a 4x faster response time at one chain led to a 3-5% increase in annual revenue.
Higher quality and satisfaction (CSAT/NPS): the faster and more accurately an agent responds to a customer, the higher the chance of a repeat purchase. AI agents that work around the clock and give accurate, fast answers boost customer and employee loyalty. For example, an HR bot at a bank that resolved 77% of requests reached an NPS of 73% and 95% positive feedback.
A clear system of metrics based on financial indicators - from reduced AHT to higher NPS and annual savings - makes it possible to treat AI agent implementation as an investment rather than an experiment and to assess how the solution generates real money.

Ethics, compliance and security: responsible AI implementation

Gartner forecasts that by 2026 more than 30% of large companies will have a dedicated AI ethics specialist due to tighter regulation.
Legal requirements for AI are changing rapidly - the EU has adopted the AI Act, and in
CIS, a similar regulatory act is being prepared.
Businesses need to build a system for responsible AI use today to avoid fines and reputational losses.

Decision transparency

. Keep the full history of the AI agent's work: what data it analyzed, what conclusions it drew, and why it made a specific decision. This will help you quickly resolve a disputed situation, for example when a bank customer challenges a loan refusal. You will be able to show exactly which criteria the system used to make the decision and avoid litigation.

Eliminating algorithmic bias

. Check regularly that the system does not discriminate against customers by age, gender, or other attributes. For example, when screening resumes automatically, the system must not lower scores for candidates of a certain gender. Run tests: compare how the agent's decisions change when protected attributes (gender, age) are altered in otherwise identical inputs. Such checks protect the company from lawsuits and reputational losses, especially when working with government clients.

Data protection

. Encrypt the personal data the agent processes and grant access only to employees who need it for their work. Update protection systems regularly and run security audits. This lets you avoid breaches and fines from Roskomnadzor, especially when handling passport data and financial information.

Liability for errors

. Spell out in advance, in your contracts with the vendor, who is liable for AI errors and the related financial losses. Build a simple procedure for customer complaints and set deadlines for handling them. If the agent makes a calculation error — for example, when issuing an insurance policy — you will have a clear plan to fix the situation quickly and compensate the damage. According to research, just 3% of inaccurate data in the training set can cut an AI model's accuracy by 50%.

This is especially critical for credit scoring systems, where data errors lead to unfair loan denials. That is why, before launching an agent, you need to thoroughly check and clean the data, as well as regularly test the system on real-world scenarios. This helps avoid financial losses and reputational risks tied to incorrect algorithm behavior.

What AI agents are, how they automate business processes, and how they differ from RPA and assistants