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

# Configure AI connections

> Choose shared model connections and override them for individual agents.

You need Admin or Owner access to configure shared connections and credentials. Open **AI connections** in the console.

## Choose the services

| Service         | Purpose                                                   |
| --------------- | --------------------------------------------------------- |
| **LLM**         | Conversation responses, variable extraction, and analysis |
| **Voice**       | Convert the agent's responses to speech                   |
| **Transcriber** | Recognize the caller's speech                             |
| **Realtime**    | Handle speech input and output through one connection     |
| **Embedding**   | Support chunked knowledge-base search                     |

Choose a provider and model from the console, select or create a named credential, and save. Available settings depend on the provider. Confirm that your credential has access to the selected model, language, and voice.

## Use workspace defaults

Saved workspace connections apply to agents that inherit them. Run a controlled test after changing a shared connection, particularly when several agents use it.

## Override an agent's connections

Open the agent's settings and its model overrides. Enable the override for only the services you want to change. A service without an override continues to use the workspace default.

For example, you can use a different voice for one agent while keeping the workspace LLM and transcriber. Save the agent and test before publishing.

## Realtime connections

Select a realtime provider when you want it to handle speech input and output. You do not need a separate voice and transcriber for the realtime conversation. Keep an LLM configured for extraction and QA.

For Google AI Studio, use a Gemini API key with access to the selected realtime model. For Vertex AI, provide the project, location, and service-account JSON required by the connection form. Your Google Cloud administrator should provide credentials with access to the selected model and region.

## Custom Models

Use **Custom Models** to connect a compatible endpoint your organization operates or subscribes to. Enter the exact model ID and Base URL for each service you use. Voice also needs a supported voice ID.

Select a saved credential, create one, or choose **No authentication** only when the endpoint intentionally supports it. The endpoint must support the relevant chat, speech, transcription, or embedding API. For chunked retrieval, embedding vectors must have 1,536 dimensions; reindex documents when changing the embedding model.

## Verify a connection

Save the configuration, then test an agent that uses it. Check spoken responses, transcription, tool calls, and analysis as applicable. Saving a valid credential alone does not establish that the complete conversation behaves as intended.