Vertex AI Agent Builder

Integrate Root Signals evaluations with Google Cloud's Vertex AI Agent Builder to monitor and improve your conversational AI agents in real-time.

Architecture Overview

[Vertex AI Agent Builder]
     |
     |—→ [Webhook call (to Cloud Function / Cloud Run)]
                  |
                  |—→ [Root Signals API]
                  |
                  |—→ [Evaluate response]
                  |
           [Log result / augment reply]
                  |
     ←——————— Reply to Agent Builder user

---

🔧 Step-by-Step Integration

1. Set up a webhook in Vertex AI Agent Builder

• Go to "Manage Fulfillment" in the Agent Builder UI.

• Create a webhook (can be a Cloud Function, Cloud Run, or any HTTP endpoint).

• This webhook will receive request and response pairs from user interactions.

---

2. Create a middleware endpoint (Cloud Function or Cloud Run)

This endpoint will:

• Receive user input and the LLM response.

• Construct an evaluator call to Root Signals API.

• Send the result back as part of the webhook response (optional).

Option 1: Using Built-in Evaluators

app.post('/evaluate', async (req, res) => {
  const userInput = req.body.sessionInfo.parameters.input;
  const modelResponse = req.body.fulfillmentResponse.messages[0].text.text[0];

  // Use a built-in evaluator (e.g., Relevance)
  const evaluatorPayload = {
    request: userInput,
    response: modelResponse,
  };

  const evaluatorResult = await fetch('https://api.app.rootsignals.ai/v1/skills/evaluator/execute/YOUR_EVALUATOR_ID/', {
    method: 'POST',
    headers: {
      'Authorization': 'Api-Key YOUR_API_KEY',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify(evaluatorPayload),
  });

  const result = await evaluatorResult.json();
  console.log('Evaluator Score:', result.score);

  // Return modified response (if needed)
  res.json({
    fulfillment_response: {
      messages: [
        {
          text: {
            text: [
              `${modelResponse} (Quality score: ${result.score.toFixed(2)})`
            ]
          }
        }
      ]
    }
  });
});

Option 2: Using Custom Judges

app.post('/evaluate', async (req, res) => {
  const userInput = req.body.sessionInfo.parameters.input;
  const modelResponse = req.body.fulfillmentResponse.messages[0].text.text[0];

  // Use a custom judge
  const judgePayload = {
    request: userInput,
    response: modelResponse,
  };

  const judgeResult = await fetch('https://api.app.rootsignals.ai/v1/judges/YOUR_JUDGE_ID/execute/', {
    method: 'POST',
    headers: {
      'Authorization': 'Api-Key YOUR_API_KEY',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify(judgePayload),
  });

  const result = await judgeResult.json();
  console.log('Judge Score:', result.evaluator_results);

  // Return modified response (if needed)
  res.json({
    fulfillment_response: {
      messages: [
        {
          text: {
            text: [
              `${modelResponse} (Judge results: ${JSON.stringify(result.evaluator_results)})`
            ]
          }
        }
      ]
    }
  });
});

---

3. Configure evaluators and judges

Built-in Evaluators:

• Use evaluators like Relevance, Precision, Completeness, Clarity, etc.

• Get available evaluators by logging in to https://app.rootsignals.ai/

• Examples: Relevance, Truthfulness, Safety, Professional Writing

Custom Judges:

• Create custom judges that combine multiple evaluators - use https://scorable.rootsignals.ai/ to generate a judge.

• Judges provide aggregated scoring across multiple criteria