Russian Retail Company: 3 AI Agents Replaced a 5-Person Department and Cut Return Processing Time by 85%

A Russian retail company successfully implemented an orchestration of three AI agents, which completely replaced a staff of five employees. This reduced return processing time from 42 to 6 minutes and cut the operator workload by 80%.

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How Returns Were Processed Before AI Implementation

Before the multi-agent system was introduced, processing each return at the retail company (200+ stores, 15k orders per day) took an average of 42 minutes. The support department had 7 operators who manually performed the entire chain of tasks: order verification, defect photo analysis, logistics coordination, customer response, and 1C data updates. The error rate reached 14% due to incorrect categorization or missed steps.

What Was Implemented: Orchestration of Three AI Agents

The company implemented a three-level AI agent orchestration architecture:

• Coordinator Agent. Acts as a "project manager." It receives customer requests, breaks them down into subtasks, and distributes them among specialized agents. It uses routing rules based on keywords (e.g., "return" directs to the returns agent).
• Specialized Agents. Three highly specialized agents, each responsible for their specific area of work, such as a returns agent, a quality agent, and a communications agent. They handle specific tasks like verifying order status, analyzing defect photos, or updating system data.
• Memory and Learning System. Includes short-term memory (current dialogue context) and long-term memory (database of resolved cases). When errors occur, an operator flags the solution, and agents are retrained on these examples daily.

The system was integrated with 1C:UT 11.4 via REST API, an email server, and internal photo storage. 17 business routing rules were configured, for example, "return without defect under ₽1000" was automatically approved.

Results and Benefits

The implementation of the multi-agent system brought significant improvements:

• Reduced Return Processing Time. The time spent on one return was cut from 42 to 6 minutes, representing an 85% saving.
• Reduced Operator Workload. The workload on support operators decreased by 80%. They now focus on non-standard cases rather than routine operations.
• Increased Accuracy. Specialized agents achieved over 90% accuracy on narrow tasks, while "monolithic" agents in previous tests showed only 65%.
• Staff Savings. Three AI agents completely replaced five full-time employees, allowing for the reallocation of company resources.

Agent orchestration proved effective where the process consists of several sequential stages, there is access to key system APIs, and 40% of employee time is spent on predictable routine tasks.

Source: habr.com

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