> This page is for SWML. > Fetch clean Markdown by appending `.md` to any page URL under https://signalwire.com/docs or requesting it with the HTTP header `Accept: text/markdown`. The root index at https://signalwire.com/docs/llms.txt lists the available documentation indexes. # amazon_bedrock > Create an Amazon Bedrock agent interaction. [params]: /docs/swml/reference/calling/amazon-bedrock/params [prompt]: /docs/swml/reference/calling/amazon-bedrock/prompt [SWAIG]: /docs/swml/reference/calling/amazon-bedrock/swaig [set_global_data action]: /docs/swml/reference/calling/amazon-bedrock/swaig/functions/data-map#actions [prompt-engineering]: /docs/platform/ai/prompt-engineering Create an Amazon Bedrock agent with a prompt. Since the text prompt is central to getting great results out of the AI, it is highly recommended that you also read the [prompt engineering][prompt-engineering] guide. ## **Properties** **`amazon_bedrock`** `object` — required An object that accepts the following properties. --- **`amazon_bedrock.global_data`** `object` A powerful and flexible environmental variable which can accept arbitrary data that is set initially in the SWML script or from the SWML [`set_global_data` action][set_global_data action]. This data can be referenced **globally**. All contained information can be accessed and expanded within the prompt - for example, by using a template string. --- **`amazon_bedrock.params`** `object` A JSON object containing [`parameters`][params] as key-value pairs. --- **`amazon_bedrock.post_prompt`** `object` The final set of instructions and configuration settings to send to the agent. Accepts either a `text` string or a `pom` object array for structured prompts, plus optional tuning parameters. See [post\_prompt details](#post_prompt) below. --- **`amazon_bedrock.post_prompt_url`** `string` The URL to which to send status callbacks and reports. Authentication can also be set in the url in the format of `username:password@url`. See [post\_prompt\_url callback](#post_prompt_url-callback) below. --- **`amazon_bedrock.prompt`** `object` — required Establishes the initial set of instructions and settings to configure the agent. See [`prompt`][prompt] for additional details. --- **`amazon_bedrock.SWAIG`** `object` An array of JSON objects to create user-defined functions/endpoints that can be executed during the dialogue. See [`SWAIG`][SWAIG] for additional details. --- ## post\_prompt The `post_prompt` object accepts either a plain text prompt or a structured POM prompt, plus optional tuning parameters. #### Regular prompt **`post_prompt.text`** `string` — required The main identity prompt for the AI. This prompt will be used to outline the agent's personality, role, and other characteristics. --- **`post_prompt.temperature`** `number` Controls the randomness of responses. Higher values (e.g., 0.8) make output more random and creative, while lower values (e.g., 0.2) make it more focused and deterministic. Range: 0.0 to 1.0. --- **`post_prompt.top_p`** `number` Controls diversity via nucleus sampling. Only tokens with cumulative probability up to top\_p are considered. Lower values make output more focused. Range: 0.0 to 1.0. --- **`post_prompt.confidence`** `number` Minimum confidence threshold for AI responses. Responses below this threshold may be filtered or flagged. Range: 0.0 to 1.0. --- **`post_prompt.presence_penalty`** `number` Penalizes tokens based on whether they appear in the text so far. Positive values encourage the model to talk about new topics. --- **`post_prompt.frequency_penalty`** `number` Penalizes tokens based on their frequency in the text so far. Positive values decrease the likelihood of repeating the same line verbatim. --- #### POM prompt **`post_prompt.pom`** `object[]` — required An array of objects that defines the prompt object model (POM) for the AI. The POM is a structured data format for organizing and rendering a prompt for the AI agent. This prompt will be used to define the AI's personality, role, and other characteristics. See the [`POM technical reference`](/docs/swml/reference/calling/amazon-bedrock/prompt) for more information. --- **`post_prompt.temperature`** `number` Controls the randomness of responses. Higher values (e.g., 0.8) make output more random and creative, while lower values (e.g., 0.2) make it more focused and deterministic. Range: 0.0 to 1.0. --- **`post_prompt.top_p`** `number` Controls diversity via nucleus sampling. Only tokens with cumulative probability up to top\_p are considered. Lower values make output more focused. Range: 0.0 to 1.0. --- **`post_prompt.confidence`** `number` Minimum confidence threshold for AI responses. Responses below this threshold may be filtered or flagged. Range: 0.0 to 1.0. --- **`post_prompt.presence_penalty`** `number` Penalizes tokens based on whether they appear in the text so far. Positive values encourage the model to talk about new topics. --- **`post_prompt.frequency_penalty`** `number` Penalizes tokens based on their frequency in the text so far. Positive values decrease the likelihood of repeating the same line verbatim. --- ## post\_prompt\_url callback SignalWire sends the report to your `post_prompt_url` as an HTTP `POST`. See the [Amazon Bedrock post-prompt callback](/docs/apis/rest/webhooks/bedrock-post-prompt-callback) webhook page for the full field reference. ### Responding to post prompt requests Answer with any 2xx status. The body is not read, so `{"response": "ok"}` is as good as an empty one. ## Amazon Bedrock example The following example selects Bedrock's **Tiffany** voice using the [voice\_id](/docs/swml/reference/calling/amazon-bedrock/prompt) parameter in the prompt. It includes scaffolding for a [post\_prompt\_url](#properties) as well as several remote and inline functions using [SWAIG](/docs/swml/reference/calling/amazon-bedrock/swaig). #### YAML ```yaml --- version: 1.0.0 sections: main: - amazon_bedrock: post_prompt_url: https://example.com/my-api prompt: voice_id: tiffany text: | You are a helpful assistant that can provide information to users about a destination. At the start of the conversation, always ask the user for their name. You can use the appropriate function to get weather information. post_prompt: text: Summarize the conversation. SWAIG: defaults: web_hook_url: https://example.com/my-webhook functions: - function: get_weather description: To determine what the current weather is in a provided location. parameters: properties: location: type: string description: The name of the city to find the weather from. type: object - function: summarize_conversation description: Summarize the conversation. parameters: type: object properties: name: type: string description: The name of the user. ``` #### JSON ```json { "version": "1.0.0", "sections": { "main": [ { "amazon_bedrock": { "post_prompt_url": "https://example.com/my-api", "prompt": { "voice_id": "tiffany", "text": "You are a helpful assistant that can provide information to users about a destination.\nAt the start of the conversation, always ask the user for their name.\nYou can use the appropriate function to get weather information.\n" }, "post_prompt": { "text": "Summarize the conversation." }, "SWAIG": { "defaults": { "web_hook_url": "https://example.com/my-webhook" }, "functions": [ { "function": "get_weather", "description": "To determine what the current weather is in a provided location.", "parameters": { "properties": { "location": { "type": "string", "description": "The name of the city to find the weather from." } }, "type": "object" } }, { "function": "summarize_conversation", "description": "Summarize the conversation.", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "The name of the user." } } } } ] } } } ] } } ```