***

id: 3fb570ef-7b0e-4341-8a8e-1c270fd6bedc
slug: /reference/amazon-bedrock
title: amazon\_bedrock
description: Create an Amazon Bedrock agent interaction.
max-toc-depth: 3
----------------

[params]: /docs/swml/reference/amazon-bedrock/params

[prompt]: /docs/swml/reference/amazon-bedrock/prompt

[SWAIG]: /docs/swml/reference/amazon-bedrock/swaig

[set_global_data action]: /docs/swml/reference/amazon-bedrock/swaig/functions/data-map#actions

[Prompting Best Practices]: /docs/platform/ai/best-practices#crafting-the-initial-prompt-for-the-ai

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 [Prompting Best Practices][Prompting Best Practices] guide.

## **Properties**

<ParamField path="amazon_bedrock" type="object" required={true} toc={true}>
  An object that accepts the following properties.
</ParamField>

<Indent>
  <ParamField path="amazon_bedrock.global_data" type="object" toc={true}>
    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.
  </ParamField>

  <ParamField path="amazon_bedrock.params" type="object" toc={true}>
    A JSON object containing [`parameters`][params] as key-value pairs.
  </ParamField>

  <ParamField path="amazon_bedrock.post_prompt" type="object" toc={true}>
    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.
  </ParamField>

  <ParamField path="amazon_bedrock.post_prompt_url" type="string" toc={true}>
    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.
  </ParamField>

  <ParamField path="amazon_bedrock.prompt" type="object" required={true} toc={true}>
    Establishes the initial set of instructions and settings to configure the agent.

    See [`prompt`][prompt] for additional details.
  </ParamField>

  <ParamField path="amazon_bedrock.SWAIG" type="object" toc={true}>
    An array of JSON objects to create user-defined functions/endpoints that can be executed during the dialogue.

    See [`SWAIG`][SWAIG] for additional details.
  </ParamField>
</Indent>

## post\_prompt

The `post_prompt` object accepts either a plain text prompt or a structured POM prompt, plus optional tuning parameters.

<Tabs>
  <Tab title="Regular prompt">
    <ParamField path="post_prompt.text" type="string" required={true} toc={true}>
      The main identity prompt for the AI. This prompt will be used to outline the agent's personality, role, and other characteristics.
    </ParamField>

    <ParamField path="post_prompt.temperature" type="number" toc={true}>
      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.
    </ParamField>

    <ParamField path="post_prompt.top_p" type="number" toc={true}>
      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.
    </ParamField>

    <ParamField path="post_prompt.confidence" type="number" toc={true}>
      Minimum confidence threshold for AI responses. Responses below this threshold may be filtered or flagged. Range: 0.0 to 1.0.
    </ParamField>

    <ParamField path="post_prompt.presence_penalty" type="number" toc={true}>
      Penalizes tokens based on whether they appear in the text so far. Positive values encourage the model to talk about new topics.
    </ParamField>

    <ParamField path="post_prompt.frequency_penalty" type="number" toc={true}>
      Penalizes tokens based on their frequency in the text so far. Positive values decrease the likelihood of repeating the same line verbatim.
    </ParamField>
  </Tab>

  <Tab title="POM prompt">
    <ParamField path="post_prompt.pom" type="object[]" required={true} toc={true}>
      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/amazon-bedrock/prompt) for more information.
    </ParamField>

    <ParamField path="post_prompt.temperature" type="number" toc={true}>
      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.
    </ParamField>

    <ParamField path="post_prompt.top_p" type="number" toc={true}>
      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.
    </ParamField>

    <ParamField path="post_prompt.confidence" type="number" toc={true}>
      Minimum confidence threshold for AI responses. Responses below this threshold may be filtered or flagged. Range: 0.0 to 1.0.
    </ParamField>

    <ParamField path="post_prompt.presence_penalty" type="number" toc={true}>
      Penalizes tokens based on whether they appear in the text so far. Positive values encourage the model to talk about new topics.
    </ParamField>

    <ParamField path="post_prompt.frequency_penalty" type="number" toc={true}>
      Penalizes tokens based on their frequency in the text so far. Positive values decrease the likelihood of repeating the same line verbatim.
    </ParamField>
  </Tab>
</Tabs>

## post\_prompt\_url callback

SignalWire will make a request to the `post_prompt_url` with the following parameters:

<ParamField path="action" type="string" toc={true}>
  Action that prompted this request. The value will be "post\_conversation".
</ParamField>

<ParamField path="ai_end_date" type="integer" toc={true}>
  Timestamp indicating when the AI session ended.
</ParamField>

<ParamField path="ai_session_id" type="string" toc={true}>
  A unique identifier for the AI session.
</ParamField>

<ParamField path="ai_start_date" type="integer" toc={true}>
  Timestamp indicating when the AI session started.
</ParamField>

<ParamField path="app_name" type="string" toc={true}>
  Name of the application that originated the request.
</ParamField>

<ParamField path="call_answer_date" type="integer" toc={true}>
  Timestamp indicating when the call was answered.
</ParamField>

<ParamField path="call_end_date" type="integer" toc={true}>
  Timestamp indicating when the call ended.
</ParamField>

<ParamField path="call_id" type="string" toc={true}>
  ID of the call.
</ParamField>

<ParamField path="call_log" type="object" toc={true}>
  The complete log of the call, as a JSON object.
</ParamField>

<Indent>
  <ParamField path="call_log.content" type="string" toc={true}>
    Content of the call log entry.
  </ParamField>

  <ParamField path="call_log.role" type="string" toc={true}>
    Role associated with the call log entry (e.g., "system", "assistant", "user").
  </ParamField>
</Indent>

<ParamField path="call_start_date" type="integer" toc={true}>
  Timestamp indicating when the call started.
</ParamField>

<ParamField path="caller_id_name" type="string" toc={true}>
  Name associated with the caller ID.
</ParamField>

<ParamField path="caller_id_num" type="string" toc={true}>
  Number associated with the caller ID.
</ParamField>

<ParamField path="content_disposition" type="string" toc={true}>
  Disposition of the content.
</ParamField>

<ParamField path="content_type" type="string" toc={true}>
  Type of content. The value will be `text/swaig`.
</ParamField>

<ParamField path="conversation_id" type="string" toc={true}>
  A unique identifier for the conversation thread, if configured via the AI parameters.
</ParamField>

<ParamField path="post_prompt_data" type="object" toc={true}>
  The answer from the AI agent to the `post_prompt`. The object contains the three following fields.
</ParamField>

<Indent>
  <ParamField path="post_prompt_data.parsed" type="object" toc={true}>
    If a JSON object is detected within the answer, it is parsed and provided here.
  </ParamField>

  <ParamField path="post_prompt_data.raw" type="string" toc={true}>
    The raw data answer from the AI agent.
  </ParamField>

  <ParamField path="post_prompt_data.substituted" type="string" toc={true}>
    The answer from the AI agent, excluding any JSON.
  </ParamField>
</Indent>

<ParamField path="project_id" type="string" toc={true}>
  ID of the Project.
</ParamField>

<ParamField path="space_id" type="string" toc={true}>
  ID of the Space.
</ParamField>

<ParamField path="SWMLVars" type="object" toc={true}>
  A collection of variables related to SWML.
</ParamField>

<ParamField path="swaig_log" type="object" toc={true}>
  A log related to SWAIG functions.
</ParamField>

<ParamField path="total_input_tokens" type="integer" toc={true}>
  Represents the total number of input tokens.
</ParamField>

<ParamField path="total_output_tokens" type="integer" toc={true}>
  Represents the total number of output tokens.
</ParamField>

<ParamField path="version" type="string" toc={true}>
  Version number.
</ParamField>

### Post prompt callback request example

Below is a json example of the callback request that is sent to the `post_prompt_url`:

```json
{
  "total_output_tokens": 119,
  "caller_id_name": "[CALLER_NAME]",
  "SWMLVars": {
    "ai_result": "success",
    "answer_result": "success"
  },
  "call_start_date": 1694541295773508,
  "project_id": "[PROJECT_ID]",
  "call_log": [
    {
      "content": "[AI INITIAL PROMPT/INSTRUCTIONS]",
      "role": "system"
    },
    {
      "content": "[AI RESPONSE]",
      "role": "assistant"
    },
    {
      "content": "[USER RESPONSE]",
      "role": "user"
    }
  ],
  "ai_start_date": 1694541297950440,
  "call_answer_date": 1694541296799504,
  "version": "2.0",
  "content_disposition": "Conversation Log",
  "conversation_id": "[CONVERSATION_ID]",
  "space_id": "[SPACE_ID]",
  "app_name": "swml app",
  "swaig_log": [
    {
      "post_data": {
        "content_disposition": "SWAIG Function",
        "conversation_id": "[CONVERSATION_ID]",
        "space_id": "[SPACE_ID]",
        "meta_data_token": "[META_DATA_TOKEN]",
        "app_name": "swml app",
        "meta_data": {},
        "argument": {
          "raw": "{\n  \"target\": \"[TRANSFER_TARGET]\"\n}",
          "substituted": "",
          "parsed": [
            {
              "target": "[TRANSFER_TARGET]"
            }
          ]
        },
        "call_id": "[CALL_ID]",
        "content_type": "text/swaig",
        "ai_session_id": "[AI_SESSION_ID]",
        "caller_id_num": "[CALLER_NUMBER]",
        "caller_id_name": "[CALLER_NAME]",
        "project_id": "[PROJECT_ID]",
        "purpose": "Use to transfer to a target",
        "argument_desc": {
          "type": "object",
          "properties": {
            "target": {
              "description": "the target to transfer to",
              "type": "string"
            }
          }
        },
        "function": "transfer",
        "version": "2.0"
      },
      "command_name": "transfer",
      "epoch_time": 1694541334,
      "command_arg": "{\n  \"target\": \"[TRANSFER_TARGET]\"\n}",
      "url": "https://example.com/here",
      "post_response": {
        "action": [
          {
            "say": "This is a say message!"
          },
          {
            "SWML": {
              "sections": {
                "main": [
                  {
                    "connect": {
                      "to": "+1XXXXXXXXXX"
                    }
                  }
                ]
              },
              "version": "1.0.0"
            }
          },
          {
            "stop": true
          }
        ],
        "response": "transferred to [TRANSFER_TARGET], the call has ended"
      }
    }
  ],
  "total_input_tokens": 5627,
  "caller_id_num": "[CALLER_NUMBER]",
  "call_id": "[CALL_ID]",
  "call_end_date": 1694541335435503,
  "content_type": "text/swaig",
  "action": "post_conversation",
  "post_prompt_data": {
    "substituted": "[SUMMARY_MESSAGE_PLACEHOLDER]",
    "parsed": [],
    "raw": "[SUMMARY_MESSAGE_PLACEHOLDER]"
  },
  "ai_end_date": 1694541335425164,
  "ai_session_id": "[AI_SESSION_ID]"
}
```

### Responding to post prompt requests

The response to the callback request should be a JSON object with the following parameters:

```json
{
  "response": "ok"
}
```

## Amazon Bedrock example

The following example selects Bedrock's **Tiffany** voice using the [voice\_id](/docs/swml/reference/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/amazon-bedrock/swaig).

<CodeBlocks>
  <CodeBlock title="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 the phone number, address,
                or weather information.
            post_prompt:
              text: Summarize the conversation.
            SWAIG:
              includes:
                - functions:
                    - get_phone_number
                    - get_address
                  url: https://example.com/functions
              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.
    ```
  </CodeBlock>

  <CodeBlock title="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 the phone number, address,\nor weather information.\n"
              },
              "post_prompt": {
                "text": "Summarize the conversation."
              },
              "SWAIG": {
                "includes": [
                  {
                    "functions": [
                      "get_phone_number",
                      "get_address"
                    ],
                    "url": "https://example.com/functions"
                  }
                ],
                "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."
                        }
                      }
                    }
                  }
                ]
              }
            }
          }
        ]
      }
    }
    ```
  </CodeBlock>
</CodeBlocks>