Russian Retailer Replaced 5 Employees with Three AI Agents: 85% Reduction in Return Processing Time

A Russian retail company with over 200 locations and 15,000 orders per day successfully replaced five department employees with three AI agents. This led to an 85% reduction in average return processing time, from 42 to 6 minutes, an 80% decrease in operator workload, and over 90% accuracy on specialized tasks.

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Before the Implementation

Prior to the multi-agent system implementation, the company's support department consisted of 7 operators. Processing a single return took an average of 42 minutes, including order verification, defect photo analysis, logistics coordination, customer response, and 1C system updates. The error rate reached 14%, leading to incorrect defect categorization and missed steps.

Previous attempts to implement universal “super-agents” yielded modest results: covering only 25-40% of scenarios, frequent freezes in non-standard situations, and constant manual correction.

What Was Implemented: Orchestration of Three Specialized Agents

Instead of a single universal AI agent, the company implemented a three-level orchestration architecture consisting of a coordinator agent and two specialized executors:

• Coordinator Agent. Acts as a “project manager”: receives customer requests, breaks them down into subtasks, distributes them among specialized agents, and collects the results. Routing is based on keywords (e.g., “return” goes to the returns agent, “defect” goes to the quality agent). The coordinator agent does not generate the final response to the client but manages the process.
• Specialized Agents. Each agent has narrow expertise. For example, one is responsible for processing returns, while another handles quality control and defect analysis based on photos. This allows for high accuracy in their specific tasks.
• Memory and Learning System. Short-term memory stores the context of the current dialogue, while long-term memory stores a database of resolved cases. Agents continuously learn from examples marked by operators as erroneous, constantly improving their performance.

The system is integrated with 1C:UT 11.4 via REST API, a mail server, and internal storage for photos. Seventeen business routing rules were developed; for example, “return without defect under 1000₽” is automatically approved.

Pilot Results Over 3 Weeks

Agent orchestration demonstrated that specialization leads to high accuracy, and the coordinator ensures seamless transitions between stages. Integration with 1C eliminated manual data entry, which was a major pain point for operators.

Pitfalls and Solutions

• Legacy Integrations. 1C:UT 11.4 lacks a native REST API, requiring the development of an intermediate adapter in Python. This added 5 days to the project timeline.
• Non-standard Photos. The quality agent made errors on blurry or poorly lit photos. Solution: added a rule “if confidence <80% — request a new photo.”
• Operator Training. In the early days, operators tried to “help” the agents, leading to duplicated efforts. A clear protocol was implemented: intervene only when the status is “requires intervention.”
• Inference Cost. The multimodal model for photo analysis proved expensive. Optimization: image compression to 512px and caching of results.

Source: habr.com

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