> ## Documentation Index
> Fetch the complete documentation index at: https://docs.overmindlab.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Serve and activate a model

> Verify a trained deployment and connect an application to its supported inference contract.

Successful training automatically starts deployment through the matching inference runtime. Start from the returned deployment ID and inspect its status. For an older successful job without a deployment, use `deploy_finetune`; deployment failure does not require retraining.

Use the deployment returned by the completed job. For the fraud example, supply a transaction in the conversation. This smoke request uses the serving provider.

Test our trained fraud model in Overmind on a few transactions and explain its predictions. Show me a working application request using the deployed model’s actual input and response format.

**What you get:** A recorded inference result and an application integration snippet for that deployment.

The steps below describe the workflow your agent follows and the results to verify.

## 1. Verify the deployed contract

Read the deployment resource and operational timeline. A chat model accepts messages. A native decision model accepts typed decisions and returns option probabilities. Use the contract recorded on the deployment.

For a smoke request, call `run_inference` with the deployment and a stable `request_key`:

* Chat: supply `messages` and an intentional output budget.
* Decisions: supply `decisions` with `state`, `question`, `kind` and ordered `options`; omit chat generation parameters.

Poll the `inference_request` receipt. Check the output, usage and errors. For chat, inspect `finish_reason` and `truncated`; for decisions, verify the option/probability mapping.

## 2. Connect the application

Use the deployment's verified API snippet or [Inference reference](/reference/inference.md). A pinned chat model ID addresses one deployment. A capability alias follows the capability's selected chat model.

For native probabilities, use the typed inference endpoint or returned deployment-specific request contract. A decision deployment is not a chat model and does not become one by using a chat alias.

## 3. Activate a chat alias when needed

When you want the selected capability's application alias to use this chat model:

Make this chat model active for the selected capability in Overmind. Verify that activation completed and show me how to confirm my application is calling it.

If the application uses `overmind/CAPABILITY_UUID`, call `set_active_model` with that capability and ready deployment. Follow the returned `model_activation` job.

Activation wakes and verifies the deployment before switching the alias. The previous selection continues serving until verification succeeds. Read the failure stage before retrying.

Send a real application API-key request through the alias after activation. A verification probe or a request pinned directly to the model does not confirm that your application adopted the alias.

## 4. Monitor the deployed workload

Inspect request success, latency, throughput and measured cost with a stated time period and traffic source. Deployment readiness, worker warmth, active routing and application connection are separate states.

For a confirmed failed deployment, use `retry_deployment` after inspecting its error. For an uncertain remote submission, reconcile the existing attempt first. See [Recover interrupted work](/how-to/recover.md).


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