Automating Retail Operations Through AI Agents Connected to Enterprise Systems

The operation relied on multiple manual processes that generated high workloads, long execution times, and increased exposure to human error. Business information was also distributed across different systems, making it difficult to manage requests efficiently.

The main challenge was to implement an AI solution that went beyond a conventional chatbot. The platform needed to understand requests, determine the required actions, interact with multiple enterprise systems, and execute operations in a controlled manner while maintaining security and validation rules.

Rootstack designed and implemented a task-oriented AI agent architecture capable of interpreting user intent, determining the required actions, accessing authorized enterprise tools, and verifying results before completing each process.

A primary AI agent was responsible for coordinating the different actions required to fulfill a request. For example, when handling an order-related request, the agent could identify the customer, retrieve the order, verify its status, check inventory, execute an authorized action, record the result, and communicate the final outcome to the user.

Unlike a conventional chatbot, the solution was designed to execute business operations rather than simply provide information. The agents could only access predefined and authorized tools, ensuring that AI interactions with enterprise systems remained controlled.

Rootstack implemented Model Context Protocol (MCP) and Tool Calling to connect the AI agents with enterprise capabilities. Each tool was exposed independently with predefined parameters, permissions, and execution rules. These tools included order and inventory inquiries, customer information retrieval, return eligibility validation, request creation, information updates, and payment and transaction inquiries.

The solution also incorporated n8n as an automation and workflow orchestration layer. This allowed AI-driven processes to connect with internal APIs, inventory systems, order management platforms, CRM systems, notification services, and third-party platforms. As a result, requests interpreted by the AI could be transformed into executable business workflows.

A microservices-based integration layer connected the agents with the organization's enterprise applications through REST APIs. This architecture kept the AI layer decoupled from the underlying systems, allowing new tools, integrations, and business capabilities to be introduced without modifying the core agent logic.

Because the agents could execute actions against real enterprise systems, Rootstack implemented authentication, role-based authorization, parameter validation, business rules, explicit confirmation for sensitive operations, and complete action logging. These controls allowed the organization to automate operational processes while maintaining governance over critical actions.

The platform also included an observability layer based on OpenTelemetry, providing visibility into incoming requests, tools selected by agents, actions executed, execution times, workflow errors, and processes requiring human intervention. This enabled continuous monitoring and optimization of both the AI agents and the underlying workflows.