Always On: Implementing Continuous Evaluation for Real-Time AI Monitoring

Your new AI agent is live. After weeks of development, testing, and rigorous pre-deployment checks, it's finally in the hands of real users. The initial metrics look great: latency is low, error rates are zero, and uptime is 100%. But are you measuring what truly matters? How do you know if your AI is still accurate, helpful, and on-brand weeks or months after launch?

Traditional application performance monitoring (APM) tells you if your service is running. It doesn't tell you if it's working. For AI systems, where "correctness" is often subjective and nuanced, this gap is where quality degrades, user trust erodes, and silent failures multiply.

This is where Continuous Evaluation comes in. It's the shift from gatekeeping quality before deployment to continuously monitoring AI performance in production. It’s the key to building resilient, reliable, and truly intelligent systems.

The Blind Spot of Pre-Deployment Testing

Let's be clear: pre-deployment evaluation is non-negotiable. Integrating AI testing into your CI/CD pipeline, a practice we call 'Evaluation-Driven Development', is fundamental for catching regressions and ensuring a baseline of quality. Using a platform like Evals.do, you can define your evaluations as code and automatically run them before any new model version goes live.

{
  "evaluationId": "pre-deploy-check_abc123",
  "target": "customer-support-agent:v1.3-beta",
  "dataset": "customer-support-queries-2024-q3",
  "summary": {
    "overallScore": 4.35,
    "pass": true,
    "metrics": {
      "accuracy": { "score": 4.1, "pass": true },
      "helpfulness": { "score": 4.4, "pass": true },
      "tone": { "score": 4.55, "pass": true }
    }
  }
}

But the real world is messy. Once deployed, your AI agent encounters challenges that no static dataset can fully anticipate:

• Data Drift: The topics, language, and intent of user queries will change over time, potentially moving outside the distribution of your original training and testing data.
• Concept Drift: The very definition of a "good" answer can evolve. A helpful response last quarter might be outdated or incomplete today due to new product features or policy changes.
• Unexpected Edge Cases: Users will interact with your agent in creative and unpredictable ways, uncovering novel failure modes.
• Silent Degradation: LLMs often fail without throwing an error. They might provide plausible but factually incorrect information, adopt an off-brand tone, or subtly misunderstand a query. These issues are invisible to traditional monitoring tools.

Without a system to watch for these issues in production, your high-performing AI can slowly and silently become a liability.

What is Continuous Evaluation?

Continuous Evaluation is the practice of systematically and automatically assessing a live AI system's performance using real-world production data. It extends the principles of CI/CD to the production environment, creating an "always on" feedback loop for AI quality.

Think of it as a specialized APM for model behavior. Instead of just tracking CPU usage and response times, you track metrics that define quality:

• Factuality & Accuracy
• Helpfulness & Relevance
• Tone & Brand Alignment
• Safety & Bias

By sampling real production traffic and running it through a defined evaluation suite, you can generate a real-time health score for your AI's qualitative performance.

Implementing a Continuous Evaluation Pipeline with Evals.do

The "Integrations" category is all about connecting systems, and Evals.do is designed to be the hub for your AI quality signals. Here’s how you can build a continuous evaluation pipeline.

Step 1: Codify Your Evaluation Criteria

The beauty of the "Business-as-Code" approach is reusability. The same evaluation suite you use for pre-deployment checks can be repurposed for production monitoring. In Evals.do, you define your metrics, grading criteria (e.g., using a separate AI model as a grader), and pass/fail thresholds in a clear, version-controlled format. This ensures consistency from testing to production.

Step 2: Instrument Your Application to Sample Traffic

You don't need to evaluate every single production request. That would be slow and expensive. Instead, instrument your application to sample a small fraction (e.g., 1-5%) of your live traffic—both the user input and the AI's response.

Then, send this data to the Evals.do API asynchronously. This ensures that your monitoring process doesn't add any latency to the user-facing request.

Here's a conceptual pseudo-code example for a Node.js server:

// Pseudo-code for a server handling AI agent requests
async function handleUserQuery(query) {
  // 1. Get the response from your live agent
  const aiResponse = await myLiveAIAgent.run(query);

  // 2. Sample a fraction of traffic for continuous evaluation
  if (Math.random() < 0.01) { // Sample 1% of requests
    // 3. Asynchronously trigger the evaluation - no impact on user latency
    fetch('https://api.evals.do/v1/evaluations', {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${process.env.EVALS_DO_API_KEY}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        // Use the same evaluation suite definition as in CI
        suiteId: "prod-quality-monitor-suite",
        // Pass the live data
        input: { query: query },
        output: { response: aiResponse.text },
        // Tag the model version for easy tracking
        target: "customer-support-agent:v1.2"
      })
    }).catch(err => console.error("Failed to trigger Evals.do evaluation:", err));
  }

  // 4. Return the response to the user immediately
  return aiResponse;
}

Step 3: Monitor Dashboards and Set Up Alerts

Within Evals.do, you can now visualize your AI's performance over time. You can track your helpfulness score, accuracy rate, and tone alignment on an hourly or daily basis.

More importantly, you can move from reactive analysis to proactive alerting. Set up rules to get notified when performance degrades, such as:

• "Alert the on-call channel if the average accuracy score drops below 4.0 for more than 30 minutes."
• "Send a daily digest if the refusal_rate for sensitive topics increases by more than 10%."

This turns your evaluation system into an active defense mechanism for AI quality.

Step 4: Close the Loop

When an alert fires, Evals.do provides the context you need to act. You can immediately see the specific inputs and outputs that led to the performance dip. This is your feedback loop.

1. Analyze: Isolate the failing samples.
2. Augment: Add these real-world failure cases to a new evaluation dataset.
3. Improve: Use this new data to fine-tune your model, update your prompt, or patch your agentic workflow.
4. Verify: Test your fix against the new dataset in your CI/CD pipeline using Evals.do.
5. Deploy: Ship the improved version with confidence.

From Fragile to Antifragile AI

By implementing continuous evaluation, you transform your AI systems from fragile components that break silently into antifragile systems that learn and improve from real-world stress. You gain the ability to not only build high-quality AI but to maintain it over time.

Pre-deployment testing gives you the confidence to launch. Continuous evaluation gives you the confidence to scale. Together, they provide a comprehensive framework for ensuring your AI functions, workflows, and agents consistently meet the highest standards of quality and reliability.

Ready to gain true, quantifiable confidence in your production AI? Explore Evals.do and build your "always on" evaluation pipeline today.