Generate Digital Humans

Given an agent this endpoint generates digital humans to test that agent based on varying simulation types.

POST /v1/generate-digital-humans

Generate Digital Humans

cURL

curl --request POST \
  --url https://api.getbluejay.ai/v1/generate-digital-humans \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <x-api-key>' \
  --data '
{
  "agent_id": "<string>",
  "simulation_id": "<string>",
  "prompt_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "knowledge_base_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "goal_adherence": [
    {
      "goal": "<string>",
      "num_calls": 123
    }
  ],
  "workflow_adherence": {},
  "workflow_adherence_v2": {},
  "replay_transcript": [
    {
      "transcript": "<string>",
      "success_criteria": "<string>",
      "mode": "smart"
    }
  ],
  "customer_personas": [
    {
      "name": "<string>",
      "description": "<string>",
      "goal": "<string>",
      "num_calls": 123
    }
  ],
  "load_testing": 0,
  "red_teaming": 0,
  "traits": [
    {
      "trait_name": "<string>",
      "trait_data_type": "BOOLEAN",
      "value": "<unknown>",
      "is_sip_header": false
    }
  ],
  "num_runs": 1
}
'

200

{
  "digital_humans": [
    "<unknown>"
  ],
  "status": "200",
  "success": true,
  "test_scenarios": [
    "<unknown>"
  ],
  "warning": "<string>"
}

Integration Prompt for AI Agents

# Bluejay — Testing & Monitoring Platform for Conversational AI Agents

You are a senior backend engineer integrating the Bluejay API. Think step-by-step: first understand the endpoint, then plan the integration, then implement with minimal changes.

## Generate Digital Humans — POST /v1/generate-digital-humans

> **What this endpoint does:** Given an agent this endpoint generates digital humans to test that agent based on varying simulation types.

**Endpoint:** POST `https://api.getbluejay.ai/v1/generate-digital-humans`
**Auth:** `X-API-Key` header
**Content-Type:** application/json

### Required Parameters
| Name | Type | Description |
|------|------|-------------|
| X-API-Key | string | API key required to authenticate requests. |
| agent_id | string | ID of the agent to be used to generate the digital humans |

Review the full parameter list at https://docs.getbluejay.ai/api-reference/endpoint/generate-digital-humans and include any optional parameters (e.g., `simulation_id`, `prompt_id`, `knowledge_base_id`, `goal_adherence`, `workflow_adherence`, `workflow_adherence_v2`) that serve your integration's use case and align with Bluejay's testing and monitoring capabilities.

### Request Body (required fields)
```json
{
  "agent_id": "string"
}
```

Refer to the full schema at https://docs.getbluejay.ai/api-reference/endpoint/generate-digital-humans. Include optional fields that serve the goal of setting up for testing and monitoring on Bluejay.

### Example
**POST with body:**
```python
import requests

def generate_digital_humans(payload: dict, api_key: str) -> dict:
    url = "https://api.getbluejay.ai/v1/generate-digital-humans"
    headers = {"X-API-Key": api_key}
    response = requests.post(url, headers=headers, json=payload)
    response.raise_for_status()
    return response.json()
```

### Constraints
- Minimal changes — only add/change files needed for this integration.
- Match existing codebase patterns (naming, file structure, error handling).
- Include error handling for 400: Request or generation constraint violation; 422: Validation Error.

### Integration Checklist
Before writing code, verify:
1. Which module/service owns this API domain in the codebase?
2. What HTTP client and error-handling patterns does the project use?
3. Are there existing types/interfaces to extend?

Then implement the integration, export it, and confirm it compiles/passes lint.

Given the simulation ID and generation options, this endpoint creates digital humans from scenarios. Payload shape and validation rules (including workflow-based generation) are documented in the OpenAPI schema on this page. For graph-based paths, see Create workflow and the Workflows cookbook.

The generate request does not accept allow_silence_tool or silence_tool_instructions; those are set when you create or update a digital human explicitly. Generated or returned digital_human objects in responses may still include those fields at model defaults (allow_silence_tool false, silence_tool_instructions "default"). Whether the silence tool runs in voice simulations is determined by the execution layer that reads stored test-case data, not by this API alone.

Headers

X-API-Key · string · required

API key required to authenticate requests.

Body

application/json

Pydantic model for digital humans (simulation type) request

agent_id · string · required

ID of the agent to be used to generate the digital humans

simulation_id · string | null

Optionally attach the digital humans to a simulation

prompt_id · string<uuid> | null

Prompt UUID associated with the simulation

knowledge_base_id · string<uuid> | null

Knowledge base UUID associated with the simulation

goal_adherence · GoalAdherence · object[] | null

List of goal adherence scenarios

Show child attributes

workflow_adherence · Workflow Adherence · object

Dictionary of workflow IDs and their counts

Show child attributes

workflow_adherence_v2 · Workflow Adherence V2 · object

Dictionary of workflow_v2 IDs and their counts (uses new graph definition format)

Show child attributes

replay_transcript · (Replay Transcript From Text · object | Replay Transcript From Conversation ID · object)[] | null

List of transcript replays to generate digital humans from

Replay transcript by providing the transcript text directly.

Replay Transcript From Text

Replay Transcript From Conversation ID

Show child attributes

customer_personas · CustomerPersonaRequest · object[] | null · deprecated

List of customer personas to be used in the simulation

Show child attributes

load_testing · integer | null · default: 0

Number of load testing calls

red_teaming · integer | null · default: 0

Number of red teaming calls

traits · Trait · object[] | null

List of traits to apply to all generated digital humans

Show child attributes

num_runs · integer | null · default: 1

Number of runs per digital human per simulation run (run count).

Required range: x >= 1

Response

200

application/json

Successful Response

digital_humans · any[]

List of digital humans created for the simulation

status · string · default: 200

Status of the response

success · boolean · default: true

Whether the generation succeeded

test_scenarios · any[] | null

Scenario view of created digital humans (includes original_transcript and formatted_transcript for transcript replays)

warning · string | null

Warning message if the request was modified (e.g. truncated due to exceeding the max limit)