For Lato Bicycles, innovation and efficiency are always in motion. Like many Mendix customers, Lato uses Mendix Portfolio Management to track their initiatives. This strategic overview allowed them to identify areas ripe for innovation and efficiency gains and track them through to deployment and into ongoing operation. During a recent portfolio review, the leadership team spotted a bottleneck in their customer service process: the initial triage of Return Requests.
Each request arrived as unstructured text via a web form, requiring customer service reps to manually read, categorize, and determine next steps. The process was repetitive, time-consuming, and sometimes delayed resolution.
Using Mendix, Lato set out to design their first AI agent: A Returns Request Assistant that could analyze incoming requests, summarize key details, and prepare repair plans automatically.
- Lato Bikes used Mendix to automate Returns triage, turning unstructured customer data into actionable insights.
- Starting small with a single event-driven agent helped the team prove value and refine their approach.
- Prompt clarity and thoughtful tool design ensured accurate, reliable agent behavior.
- With Mendix Agent Builder and Portfolio Management, Lato tracked performance and scaled their AI strategy confidently.
Defining measurable outcomes
Before development began, Lato defined what success meant for the project. Their goals were clear:
- Reduce manual triage time by 40%.
- Achieve 95% accuracy in categorizing return requests.
- Cut claim resolution time from 14 days to 3 days.
These KPIs were tracked directly in Mendix Portfolio Management, ensuring the project stayed aligned with tangible business outcomes.
Because Return Requests arrive sporadically, the team opted for an event-driven agent that activates whenever a new request is submitted in the Return Request Portal.
The portal was already integrated with key systems like Customer Order Management and Product Inventory, making it an ideal entry point for the agent. Since the agent wouldn’t be running fully autonomously, user interactions with the agent were set to go through user tasks that the agent generates, with out-of-the-box observability for system administrators.
By embedding automation where employees already worked, Lato minimized disruption and accelerated adoption.
Lato chose Claude 3.5 to power the agent. This provided:
- Strong tool-use capabilities
- Balanced performance and cost
- Simplified procurement and monitoring
While more advanced models were available, Lato’s core needs—tool use, summarization, and decision support—were fully met by Claude 3.5. Furthermore, by leveraging Mendix’s Cloud Gen AI Resource Packs, Lato was able to streamline the entire process. The resource packs:
- Simplified the purchasing and procurement of the model.
- Provided a straightforward, low-code integration path for their development team.
- Included built-in usage monitoring, giving them a clear view of their data consumption and costs from the start.
- Were covered by Lato’s existing Data Processing Agreement with Mendix.
Once the underlying model was decided on, the next crucial step was prompt engineering: structuring the data and instructions within the agent’s prompt to elicit the desired behavior.
Agents typically operate using two primary types of prompts: the system prompt and the user prompt. The system prompt is a set of instructions the developer provides to define the agent’s persona, overall goal, constraints, and how it should behave. System prompts contain the core instructions that set the stage for the entire interaction.
A powerful agent needs tools. These tools are specialized functions, like microflows in a Mendix application, that the agent can call upon to perform specific actions. The agent has a list of available tools, each with a clear name and description. Given a task, the agent’s job is to orchestrate using these tools to achieve the desired outcome.
Lato began with one essential capability: retrieving customer data using a REST call based on the customer’s email. Using Mendix Agent Builder, they:
Agent development is an iterative process, not a one-shot deal. It’s crucial to start with a baseline model, assess it against the KPIs laid out, and then rigorously test the agent across a wide range of scenarios.
When users began to use the agent, they found it frequently failed to locate parts. The agent’s searches were hindered by spelling errors in the incoming requests and data quality issues in both the Product Inventory and Customer Order Management systems.
To rectify this, a new tool was introduced that allowed the agent to look up parts by their serial number. This provided a reliable alternative for finding parts when the name-based search failed.
The team then used the testing capabilities in Agent Commons to review the changes against specific test cases, which allowed them to evaluate the improvement. The more robust agent was deployed as a new version and monitored to ensure its continued success and to identify further potential improvements.
But agents operate on the principle of “anything not stated will be assumed.” This means that ambiguity in a prompt or tool definitions will be filled in by the agent’s inherent knowledge, which can lead to unpredictable and undesirable outcomes. It’s crucial to be specific about the agent’s goals, constraints, and the order of operations.
The Lato Return Request agent was pulling together information in a helpful way, but to take the automation to the next level, Lato wanted it to draft an email that the CSR could review and send with a single click. For this, a new tool was introduced to draft an email and initiate a workflow.
A human should always be in the loop for any decision with significant consequences. While agents are excellent at automating routine tasks, they lack the intuition and ethical judgment required for high-stakes decisions.
Lato designed their system so that all customer communications and repair actions require manual approval.
- The EmailDraft tool prepares responses but doesn’t send them.
- The RepairBookIn_Initiate tool triggers a workflow that routes to a CSR for validation.
- and ensures automation never bypasses human judgment for high-stakes decisions.
