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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 guide.

Properties ​

amazon_bedrock ​

objectRequired

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. 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 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 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 below.

amazon_bedrock.prompt ​

objectRequired

Establishes the initial set of instructions and settings to configure the agent.

See 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 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 ​
POM prompt ​
post_prompt.text ​

stringRequired

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.

post_prompt_url callback ​

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

action ​

string

Action that prompted this request. The value will be “post_conversation”.

ai_end_date ​

integer

Timestamp indicating when the AI session ended.

ai_session_id ​

string

A unique identifier for the AI session.

ai_start_date ​

integer

Timestamp indicating when the AI session started.

app_name ​

string

Name of the application that originated the request.

call_answer_date ​

integer

Timestamp indicating when the call was answered.

call_end_date ​

integer

Timestamp indicating when the call ended.

call_id ​

string

ID of the call.

call_log ​

object

The complete log of the call, as a JSON object.

call_log.content ​

string

Content of the call log entry.

call_log.role ​

string

Role associated with the call log entry (e.g., “system”, “assistant”, “user”).

call_start_date ​

integer

Timestamp indicating when the call started.

caller_id_name ​

string

Name associated with the caller ID.

caller_id_num ​

string

Number associated with the caller ID.

content_disposition ​

string

Disposition of the content.

content_type ​

string

Type of content. The value will be text/swaig.

conversation_id ​

string

A unique identifier for the conversation thread, if configured via the AI parameters.

post_prompt_data ​

object

The answer from the AI agent to the post_prompt. The object contains the three following fields.

post_prompt_data.parsed ​

object

If a JSON object is detected within the answer, it is parsed and provided here.

post_prompt_data.raw ​

string

The raw data answer from the AI agent.

post_prompt_data.substituted ​

string

The answer from the AI agent, excluding any JSON.

project_id ​

string

ID of the Project.

space_id ​

string

ID of the Space.

SWMLVars ​

object

A collection of variables related to SWML.

swaig_log ​

object

A log related to SWAIG functions.

total_input_tokens ​

integer

Represents the total number of input tokens.

total_output_tokens ​

integer

Represents the total number of output tokens.

version ​

string

Version number.

Post prompt callback request example ​

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

{
  "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:

{
  "response": "ok"
}

Amazon Bedrock example ​

The following example selects Bedrock’s Tiffany voice using the voice_id parameter in the prompt. It includes scaffolding for a post_prompt_url as well as several remote and inline functions using SWAIG.

YAMLJSON

---
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.

SignalWire Developer Documentation

Internal SOP Reference