From prototype to production in eight steps

Most agents start as a script that works on the developer’s own account. Turning it into something your users can rely on is a different job. Here is the path with Agent200, from first key to first real user, in eight steps.

1. Create an account and a project

Set up the project for your application and generate an API key. Start in a development environment.

2. Select the services

Enable the services your agent needs and, inside each, only the endpoints it uses. Choose the OAuth scopes to match: read-only where reading is enough.

3. Build your capabilities

Combine the endpoints that always go together into workflows with clear names, such as brief or customer_context. The model gets a few well-described tools instead of many raw endpoints.

4. Integrate the SDK

Add the SDK to your existing application and configure the API key from your environment, never from source code. Your framework and your architecture stay as they are.

5. Connect end users

When a user needs a service they have not connected, Agent200 returns an authorization URL, or sends them through hosted authorization. Design that moment into your product: explain what the agent will do with access, then ask.

6. Make requests

Conceptual example.

const result = await agent200
  .user(currentUser.id)
  .run({
    model: "openai/gpt-5.6-sol",
    instructions: instructionsFor(task),
    input: task.input,
    tools: toolsFor(task),
  });

Choose the model and the tools per task, not once for the whole app.

7. Let Agent200 execute

The model chooses capabilities, Agent200 performs the operations for that user, and tool results go back to the model until the task is complete.

8. Use the result

Agent200 returns the finished result to your application. Show it, store it, or pass it to the next step of your own workflow.

Before your first real user

• Environments: production has its own key and only the endpoints the live agent uses.
• Writes: every write operation has been tested in staging, and your app decides when a person confirms an action.
• Identity: every request passes a stable user ID resolved on your server.
• Timing: anything recurring is started by your own scheduler.
• A test set: a handful of real tasks with expected answers, run whenever you change a prompt, a tool or a model.

The prototype proves the agent can do the job. Production proves it does the job for every user, on their own data, every time.

Ready to see it with your own use case? Book a demo with the team.