Connectivity answers only one question
An API tells a client how to invoke an operation. It does not decide when a process should start, which information is missing or whether a person must approve the next step. Listing available endpoints is useful groundwork, not a complete transformation. Begin with a business result such as a reviewed quote, then identify the operations needed to reach it.
Keep ordinary logic where it belongs
AI can interpret an unstructured request while a normal function calculates a price from approved rules. The application can check stock or capacity without asking a model to invent an answer. A person can handle exceptions. Combining these elements deliberately is often more useful than replacing every step with a model call. Explain the responsibility of each step in the specification.
Make permission part of the process
Reading a record, preparing a draft and sending a commitment have different consequences. Assign an action class to each operation and define the conditions for human approval. The model’s suggestion cannot expand its own authority. Incoming documents should be treated as data, not instructions that rewrite the workflow. A useful design explains both the happy path and how the process stops safely.
Deliver the process into the application
Forge’s AI Native Processes connects the chosen workflow to an approved Product specification and a development Project. The delivered module runs in the customer’s application with customer-owned AI keys. Yepsy engineers the capability; the customer retains the daily operation. That distinction keeps software delivery separate from a promise to operate the whole business.
Design the complete path from input to result
An API exposes operations; it does not decide when a business process starts, which information is missing or who must approve an exception. Start with a specific job such as preparing a service quote. Identify the arrival event, source documents, permitted data, deterministic rules and final record.
Use AI where interpretation is useful and ordinary code where the rule is fixed. A model may extract a requested service from a message, while an application function applies the price schedule. A person can handle an unusual deadline. The value comes from connecting these responsibilities, not from maximizing the number of AI calls.
| Decision | Useful requirement | Evidence |
|---|---|---|
| Trigger | A defined event starts one execution. | A reproducible input. |
| Interpretation | AI may propose structured fields. | Validation and missing-field handling. |
| Action | The app enforces rules and approval. | Correct records and denied unsafe actions. |
Where this goes wrong
A modernized codebase alone does not prove that a company’s daily work has changed. Nor should a single automated step be described as a fully autonomous business. Name the exact workflow, its remaining human steps and the conditions under which it operates.
AI-native work is a process design problem before it is an API integration task.
Turn it into a working checklist
- Define the business result.
- Assign AI, application and human steps.
- Classify read, draft and commit actions.
- Test both success and safe stop.
A concrete next step
Prepare a workflow contract containing trigger, inputs, operations, exceptions, approvals and completion criteria. Implement it inside the customer application, with its own AI credentials and runtime controls.
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