When AI Starts Writing Your System, Who Tells It What 'Done' Means?

Description and use

Using a local, version-controlled OpenAPI specification as the single source of truth aligns AI-generated code with human expectations, reduces integration bugs, and creates a feedback loop that catches gaps early.

Problem

AI-generated code can diverge on shared definitions, causing inconsistent behavior such as duplicate bookings and status mismatches.

Outcome

Using a local, version-controlled OAS as the single source of truth aligns AI-generated code with human expectations, reduces integration bugs, and creates a feedback loop that catches gaps early.

Workflow

• 1. Define business request and ask AI to propose design against existing OAS.
• 2. Review AI’s suggested changes to endpoints, schemas, and rules.
• 3. Apply approved changes to local OAS file and commit to version control.
• 4. Use the OAS as input for coding assistants and mock services during implementation.
• 5. Run contract checks and scenario tests against the spec to verify real requests.
• 6. Iterate based on mismatches or missing fields, updating the OAS accordingly.
• 7. Render documentation and publish MCP endpoints from the same OAS file.

Evidence

An OpenAPI Specification (OAS) describes endpoints, parameters, responses, and auth in a structured format.

Source

In Powerduck, you start from a business request and ask the AI to propose a design against the existing spec.

Source

The OAS does not have to stop at "design output." In a Powerduck workflow, it continues to feed the later stages: Design, Implementation, Waiting on dependencies, Verification, Delivery.

Source

The spec lives as a local file. It goes into version control, code review, and rollback alongside the code.

Source