How to Build AI Workflows with Unified Model Access

AI applications often begin with a single model call.

A developer sends a prompt, receives a response, and builds the first working feature. This is the right way to prototype quickly.

But production AI products usually do not stay that simple.

A chatbot may need fast responses. A RAG system may need stronger reasoning over documents. An AI agent may need reliable tool use. A developer tool may need better coding behavior. An automation workflow may need predictable structured output.

These workflows have different requirements.

That is why developers need a better way to organize model access.

Start with the workflow

Before choosing a model, it helps to define the workflow.

For example, an AI product may include:

• support chat
• document Q&A
• content generation
• code assistance
• agent planning
• structured extraction
• workflow automation

Each workflow may need a different model behavior.

A support chat workflow may prioritize latency. A document Q&A workflow may prioritize reasoning. An automation workflow may prioritize structured output. A developer tool may prioritize code quality.

The application should not treat every AI request as the same type of task.

Create a model access layer

A practical pattern is to place a model access layer between the product and the model provider.

Instead of calling a model directly from every feature, the application calls an internal AI layer.

For example:

js
const result = await ai.run({
  workflow: "support_chat",
  input: userMessage
});