Russian Retailer Saved 5 FTEs: How Three AI Agents Reduced Return Processing Time by 85%

A Russian retail company with a network of over 200 stores and a daily volume of 15,000 orders faced the challenge of slow and costly return processing. By implementing a system of three AI agents, the company not only reduced the average return processing time from 42 to 6 minutes but also reallocated five support department employees, decreasing the workload on the remaining operators by 80%.

In the context of a large retailer, even a seemingly minor routine operation like return processing can consume hundreds of human-hours per month and lead to direct losses due to errors and delays. Every return is not only a financial loss but also a risk to customer loyalty. When 14% of such operations are performed incorrectly, it becomes a critical issue. However, this burden can now be alleviated by entrusting it to specialized AI agents.

How Return Processing Looked Before AI Agent Implementation

Before the multi-agent system was introduced, the company's support department consisted of seven operators, each processing returns manually. One return took an average of 42 minutes. This process was multi-stage and required careful attention to detail:

• Order verification. The operator had to find the order in the system, cross-reference data, and confirm its status.
• Analysis of defect photos. Customers often sent photos of damages that needed careful examination, defect classification, and cause determination.
• Coordination with logistics. If the item needed to be returned to the warehouse, interaction with the logistics service was required to arrange transportation.
• Customer response. Preparing an email or calling the customer with the return resolution.
• Data update in 1C. Entering all changes and statuses into the accounting system.

Such manual work led to a high error rate, up to 14%, which resulted in incorrect defect categorization, missed processing steps, and consequently, delays and customer dissatisfaction.

Why Monolithic AI Solutions Failed to Deliver Results

The company had previously attempted to automate the process using universal, "monolithic" AI agents, but the results were modest. Such agents could only cover 25-40% of scenarios, often "freezing" in non-standard situations and requiring constant manual intervention. The problem lay in trying to create a single "super-agent" capable of solving all tasks at once, which led to low accuracy and inefficiency in complex, multi-step processes like return processing.

It became clear that achieving high accuracy and autonomy required a different approach—not just automating individual steps, but managing the entire process while breaking down complex tasks into simpler, specialized ones. This led the company to the concept of a multi-agent system.

How the Multi-Agent System Was Designed

The solution involved orchestrating three specialized AI agents, each performing a precisely defined role. This allowed for high accuracy on narrow tasks while ensuring flexibility in managing the entire process.

• Coordinator Agent. This agent acts as a "project manager." It receives incoming customer requests, analyzes them, breaks them down into subtasks, and distributes them among specialized agents. Routing is based on keywords and request context (e.g., a request containing "return" is sent to the returns agent, "defect" to the quality agent). The coordinator agent does not generate the final customer response; its role is to manage the process and collect results from other agents.
• Specialized Agents. Two main specialized agents were created:
  • Returns Processing Agent. Responsible for verifying order history, statuses, return conditions, and initiating necessary internal processes.
  • Quality Control Agent. Specializes in analyzing defect photos, determining the type of damage, its cause, and proposing solutions.
  Each of these agents possesses deep expertise in its specific area, allowing for over 90% accuracy in their particular tasks.
• Memory and Learning System. To enhance agent effectiveness, a two-tiered memory system was implemented:
  • Short-term memory. Stores the context of the current customer dialogue, enabling agents to maintain coherence and consistency in communication.
  • Long-term memory. A database of resolved cases. This database is used for agent retraining: operators flag erroneous decisions, and the system uses this data for continuous improvement.

The system was integrated with key corporate systems: with 1C:UT 11.4 via REST API, with the mail server for processing incoming requests, and with internal storage for photos, ensuring seamless data exchange. Additionally, 17 business routing rules were developed; for example, "return without defect under 1000₽" is automatically approved, further reducing manual workload.

Implementation and Overcoming Challenges

The pilot project was launched and showed initial results within three weeks. Implementation was not without its challenges, but the team successfully overcame them:

• Legacy Integrations. 1C:UT 11.4 lacked a native REST API, which required the development of an intermediate Python adapter. This added 5 days to the project timeline but allowed for system integration without replacing the main accounting system.
• Non-standard Photos. The quality agent initially made errors on blurry or poorly lit photos. The solution: a rule was added, "if confidence <80% – request a repeated photo," which significantly increased analysis accuracy.
• Operator Training. In the early days, operators tried to "help" the agents by intervening unnecessarily, leading to duplicated actions and confusion. Clear protocols were developed and implemented: operators intervene only when the status is "requires intervention," which eliminated excessive human involvement.
• Inference Cost. The multimodal model for photo analysis proved expensive to use. Optimization was achieved by compressing images to 512px and caching results, which reduced costs without compromising quality.

Implementation Results

The multi-agent system allowed the company to reallocate five out of seven support department employees to other tasks requiring human involvement. The operator workload decreased by 80%, significantly increasing their job satisfaction and reducing burnout. Most importantly, the average return processing time was cut by 85%, directly impacting customer loyalty and overall business efficiency.

How to Implement This in Your Business

The case of the Russian retailer demonstrates that even in complex and multi-stage processes, AI agents can bring significant benefits. If you have routine operations that require manual labor and are prone to errors, consider the following steps:

• Identify bottlenecks. Find processes where employees spend a lot of time on repetitive tasks, especially those with a high error rate or delays.
• Divide tasks into specialized ones. Instead of creating one universal AI agent that does everything, think about how to break down a complex process into several simpler, specialized tasks, each to be performed by a separate agent.
• Implement a coordinator agent. To manage specialized agents and ensure seamless transitions between stages, you will need a coordinator agent responsible for routing requests and collecting results.
• Ensure integration. Make sure AI agents can seamlessly exchange data with your existing corporate systems (ERP, CRM, mail servers, etc.).
• Start with a pilot and iterate. Launch the system in a small, controlled area, collect feedback, retrain the agents, and gradually scale the solution to other processes.

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