Organize the AI architecture of a product
Separate interface, data, model, tools and observation. A support-ticket example shows where to validate responses and control actions.
A “summarize with AI” button looks like a small integration. Someone must still decide which data is sent, where the response is stored, what happens during a slow request and how a user corrects a bad summary. Architecture starts with those questions, before a list of libraries.
Consider a hypothetical support SaaS that suggests a category and priority for new tickets. Its first version can show a suggestion to an agent without automatically changing the record. That choice reduces the actions the integration must control.
Trace one request
The interface requests a classification and explains whether it is waiting, running or complete. The backend verifies access to the ticket, gathers only necessary data and sends the request to the model. The response is validated before becoming a usable suggestion.
If the model returns an unknown category, invalid format or unsupported explanation, the system needs to handle it. A successful API response does not automatically make its contents valid business data.
Separate suggestion from execution
When the support agent accepts a suggestion, the update must pass the same permissions and checks as a manual edit. The server remains responsible for business rules. An instruction embedded in a ticket cannot grant additional access.
If you later add an agent with tools, provide small, clear operations. Anthropic’s tool-design guidance recommends starting with well-defined operations and evaluating them on concrete tasks. A tool for checking availability is easier to control than unrestricted query access.
Treat waiting and repetition as part of the flow
Classification may take longer than expected. Show its state and let the user continue working. For long tasks, consider background execution with an identifier for retrieving the result rather than leaving the page waiting indefinitely.
Set retry and time limits. Repeated requests should not silently create competing results. If a tool has external effects, record execution so recovering from a failure does not repeat the action.
Observe what the person can complete
Record the model, configuration, duration, validation failures, usage and cost where available. Customer-data logs should not become an indiscriminate second copy of the content. Keep what is necessary for investigation and evaluation.
Track acceptance and corrections too. A quickly generated summary may add work if staff need to reopen the entire history to verify it. A useful measure includes that effort as well as API time.
Add components for a reason
Document retrieval, queues, vector storage and multiple models can help. Each component should address an observed problem and have a maintenance owner. For ticket triage, begin with the smallest flow that makes quality measurable and errors correctable.
Use the SaaS model evaluation guide to select the generation component. Before expanding actions, review agent boundaries and responsibilities rather than simply granting more permissions.
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