T-Bank and Avito: How AI Agents Freed Employees from Routine and Increased Efficiency by 21% and 14% Respectively

Almost half of Russian companies, 46%, have already implemented or are testing autonomous AI agents, yet three-quarters of them don't know how to measure the results. This leads to a paradoxical situation: some companies achieve real savings and efficiency gains, like T-Bank, where an AI agent became one of the top 3 best operators, or Avito, which increased its average check by 14%. Others, however, spend resources on prototypes that will never go into production.

Implementing AI agents seems like a fashionable trend, but without a clear strategy and understanding of where they will bring real value, it's a budget risk. Many companies rush into testing without defining success metrics, ending up with expensive toys instead of working solutions. Meanwhile, hundreds of thousands of working hours and millions of rubles are wasted on routine tasks that can already be entrusted to an AI agent today. It's crucial to understand what distinguishes an effective implementation from wasted effort.

AI Agent vs. Chatbot: What's the Difference?

The main difference between an AI agent and a traditional chatbot lies in their approach to tasks. A chatbot operates on a "question-answer" principle: it provides a reaction to a specific input, doesn't analyze the outcome of its work, and cannot change its strategy on the fly. It's like a calculator that performs a single operation on command.

An AI agent is an autonomous system that shifts from a "question-answer" model to a "task-result" model. The difference is comparable to that between a manual tool and an employee: a tool needs every movement instructed, while a specialist only needs the final goal defined. The agent independently determines which tools to use, where to find information, and with whom to interact.

Four Key Properties of an AI Agent

For a basic language model to become a full-fledged agent, it needs four fundamental properties:

• Autonomous Planning. The agent doesn't require intermediate instructions or a chain of prompts. If given the task "prepare sales analytics for the quarter," it will independently build a logical sequence: request data from the CRM, group it by region, compare figures with the previous year, and generate a report. The process proceeds without human intervention until the final product is delivered.
• Tool Use. The agent is not limited to a chat window and can use external tools: call third-party service APIs, connect to corporate databases, send emails, or create documents. A particularly promising area is computer use, where the system interacts with program interfaces just like a human. It "sees" the screen, moves the cursor, clicks buttons, and fills out forms, which allows automating work even in software without an open API. This principle formed the basis of T-Bank's agent.
• Contextual Retention and Persistence. A typical chatbot starts each conversation from scratch. An agent has memory; it retains the state and context of previous sessions. This is critically important for business tasks that span weeks: deal сопровождение, lengthy legal document approvals, or project management. The agent understands the current stage of the process and what has been done previously.
• Feedback Loop and Self-Correction. This property is the key differentiator between an agent and advanced text auto-completion. In professional settings, this mechanism is often implemented through the ReAct (Reason + Act) framework. After each step, the agent evaluates the result. If a database query returns an error, the system doesn't stop but looks for another way to get the information. If data is insufficient to solve the task, the agent initiates an additional search. The cycle of reasoning and observing results continues until the goal is achieved.

Types of AI Agents: Which One for Your Task?

The choice of architecture depends on the complexity of the process, its predictability, and the degree of responsibility you are willing to delegate to the algorithm. Attempting to implement the "smartest" tool where a basic one suffices often leads to unwarranted budget bloat and maintenance difficulties.

Reactive Agents: Reliable Automation of Linear Processes

This is the most accessible and understandable entry point into agent technology. Such systems operate on a direct "stimulus-response" principle. They lack long-term planning, memory of past sessions, or the ability to learn on the fly. They act within rigidly defined scenarios, making their operation 100% predictable. Ideal for operational tasks: classifying incoming emails, instantly routing requests, or triggering notifications. This is the most economical option, but they cannot handle ambiguous requests.

Agents with Memory and Planning: Intelligent Assistant

Unlike reactive models, these systems consider the context of previous interactions and independently build a sequence of steps to achieve a goal. They understand what was discussed previously. For example, an analytical agent doesn't just extract numbers from a database but compares them with previous month's figures, understanding the structure of your quarterly report without extra reminders. For specialists working with documents, CRM systems, and long-term projects, this type of agent becomes a "workhorse" that handles all preparatory routine tasks.

Multi-Agent Systems: Role Separation and Scaling

When a task becomes too large or requires different competencies, a "teamwork" architecture is implemented. Roles are distributed among several specialized algorithms under the control of an orchestrator. While one agent collects data, another builds predictive models, and a third generates the final file. This approach is actively developing in logistics and financial analytics. The main advantage is flexibility: if you need to add a new function, you don't rewrite the entire system; you simply "hire" another specialized agent.

Fully Autonomous Agents: Potential, Risks, and Reality

Systems capable of making decisions and performing cycles of actions without human intervention represent the pinnacle of AI integration. Gartner predicts that by 2028, they will take control of about 15% of daily work tasks. However, most companies claiming "full autonomy" maintain strict manual control at critical stages in practice. Key challenges lie outside the technology domain, as trust issues and the high cost of potential errors come to the forefront. The most rational strategy involves a gradual transition from reactive models to planning systems with phased expansion of their powers.

Where AI Agents Are Already Working: Real-World Cases

Implementing AI agents is most justified in processes with high action repeatability, formalized data, and the ability to clearly measure results. If the difference in efficiency can be expressed in numbers, using an agent is worthwhile.

T-Bank: Agent Among the Top Three Operators

Before automation, all customer inquiries were handled manually by employees, limiting the speed of operations and scalability. The bank integrated an AI agent with Computer Use functionality. The system interacts with interfaces just like a human—reading data from the screen, filling out forms, and clicking buttons. As a result, the agent fully took over 60% of inquiries. Its work was evaluated by the same quality criteria as human actions. According to internal ratings, the AI agent entered the top 3 most effective employees, replacing dozens of human hands in typical operations.

Avito: Automation of Control at 807 Locations

The company faced the need for quality control at 807 sales points, which required enormous administrative resources. Manual creation of descriptions for ads slowed down content publication and led to inconsistent text quality. After automation, 80% of processes at sales points came under AI control. The use of AI models for description generation accelerated content work, and the average check increased by 14%. Identical repetitive processes across hundreds of locations became ideal ground for scaling the agent model.

Manufacturing Company: Millions in Savings Without a Development Team

A trading and manufacturing enterprise in Central Russia received about 10,000 inquiries monthly. Due to errors in manual CRM processing, managers missed target inquiries, leading to customer loss. The company implemented an AI agent that automatically processes incoming applications, classifies them, extracts key data, and enters it into the CRM. As a result, the number of missed inquiries decreased by 85%, and processing speed increased 5-fold. Net savings amounted to several million rubles per year, without needing to hire a development team or purchase expensive licenses for complex software.

How to Implement an AI Agent in Your Company: Where to Start

The selection of an area for automation should begin with a data audit: the higher their quality and structure, the faster the agent will start generating measurable profit. Here are the key steps:

• Identify the most common routine. A task that dozens of people do every day following one scenario—searching, verifying, preparing typical responses—is an ideal first candidate for an AI agent.
• Assess data quality. An AI agent is not magic. It needs structured and clean data to work effectively. Start with processes where data is already formalized.
• Integrate the agent into familiar tools. The less an employee has to change their habits, the faster they will start using it, and this directly impacts the return.
• Start with reactive agents. This is the most accessible entry point. Gradually expand functionality to agents with memory and planning, and then, if necessary, to multi-agent systems.
• Leave decisions to humans, preparation to the agent. The agent searches, collects, and prepares a draft, while the human reviews and makes the decision. This way, you gain speed without losing control.

If this case sounds like what's happening in your company, our manager can help: he'll analyze your business and niche for free and point out where an AI agent would bring a real result in your case. Message the manager