Integrating AI Evals into Your CI/CD Pipeline

In modern software development, the CI/CD pipeline is the guardian of quality. Automated tests, builds, and deployments ensure that only reliable, well-tested code makes it to production. But what happens when your "code" is an AI model, a complex prompt, or an agentic workflow? Traditional unit and integration tests fall short.

A change that looks harmless—a minor tweak to a prompt or an upgrade to a newer LLM version—can cause subtle but significant performance regressions. The AI might become less helpful, adopt an off-brand tone, or even start hallucinating incorrect information. Catching these issues manually is slow, subjective, and unscalable.

To ship AI features with confidence, you need to extend the same rigor of your CI/CD pipeline to your AI components. You need automated, repeatable, and quantifiable AI quality gates. This is where Evaluation-Driven Development comes in, powered by platforms like Evals.do.

The Challenge: Why Traditional Testing Fails for AI

Software is deterministic. A function add(2, 2) will always return 4. AI, particularly Large Language Models (LLMs), is probabilistic. The same input can yield slightly different outputs every time. This non-determinism breaks the classic assert_equal testing paradigm.

Key challenges include:

• Semantic Regressions: An AI might still "work" but provide answers that are factually incorrect, less helpful, or tonally inappropriate.
• Scalability: Manually checking hundreds of potential user queries against a new model version before every deployment is impossible.
• Objectivity: How do you score "helpfulness" or "tone" consistently across different testers and different times?

The solution is to stop testing for exact outputs and start evaluating against defined performance metrics. Instead of asserting output == "expected_string", you need to ask:

• On a scale of 1-5, how accurate was the response?
• Did the response adhere to our brand's tone guidelines?
• Was the answer truly helpful to the user's query?

Quantify AI Performance with Code

Evals.do allows you to treat AI evaluation as a deterministic, code-based step within your development workflow. It provides an agentic workflow platform to define, run, and analyze evaluations on your AI functions, workflows, and agents.

By defining your evaluation criteria and test datasets as code, you gain the ability to:

1. Version Control Your Tests: Store evaluation sets in Git alongside your AI application's code.
2. Automate Execution: Trigger comprehensive evaluations with a single API call.
3. Establish Quality Gates: Automatically pass or fail a build based on quantifiable AI performance scores.

This is Evaluation-Driven Development: a methodology where building and deploying AI is guided by continuous, automated performance evaluation.

A Practical Guide: Embedding Evals.do into Your CI/CD Pipeline

Let's walk through how to integrate AI quality checks into a typical CI/CD workflow (e.g., using GitHub Actions).

Step 1: Define Your Evaluation Suite

First, define what "good" looks like. This involves two parts:

• Dataset: Curate a representative set of inputs to test against. For a customer support agent, this would be a list of common, tricky, and edge-case customer queries (customer-support-queries-2024-q3).
• Metrics: Define the dimensions of quality you care about. These can be objective (e.g., Did it retrieve the correct user ID?) or subjective (e.g., Was the tone empathetic?). With Evals.do, you set named metrics and their passing thresholds. For example: accuracy > 4.0, helpfulness > 4.2, tone > 4.5.

Step 2: Trigger the Evaluation via API

In your CI/CD pipeline configuration file (e.g., .github/workflows/main.yml), add a new job that runs after your standard build and test stages. This job will make an API call to Evals.do to initiate the evaluation.

jobs:
  build:
    # ... standard build steps
  
  test:
    # ... standard unit test steps
  
  evaluate-ai-agent:
    runs-on: ubuntu-latest
    needs: [build, test]
    steps:
      - name: Trigger AI Evaluation
        id: run_eval
        run: |
          curl -s -X POST "https://api.evals.do/v1/evaluations" \
            -H "Authorization: Bearer ${{ secrets.EVALS_DO_API_KEY }}" \
            -H "Content-Type: application/json" \
            -d '{
              "target": "customer-support-agent:v1.2",
              "dataset": "customer-support-queries-2024-q3"
            }' > result.json
      
      - name: Check Evaluation Result
        run: |
          PASS_STATUS=$(jq -r '.summary.pass' result.json)
          if [ "$PASS_STATUS" != "true" ]; then
            echo "AI Evaluation Failed! Check the results on Evals.do."
            exit 1
          fi

This script triggers an evaluation against a specific version of your AI agent and then checks the result.

Step 3: Analyze the Automated Report

The API call returns a detailed report once the evaluation is complete. Evals.do runs your target AI component against the entire dataset, using your predefined metrics (often graded by a powerful LLM like GPT-4 or a human reviewer) to generate a score.

The JSON output looks like this:

{
  "evaluationId": "eval_abc123",
  "target": "customer-support-agent:v1.2",
  "dataset": "customer-support-queries-2024-q3",
  "status": "completed",
  "summary": {
    "overallScore": 4.35,
    "pass": true,
    "metrics": {
      "accuracy": {
        "score": 4.1,
        "pass": true,
        "threshold": 4.0
      },
      "helpfulness": {
        "score": 4.4,
        "pass": true,
        "threshold": 4.2
      },
      "tone": {
        "score": 4.55,
        "pass": true,
        "threshold": 4.5
      }
    }
  },
  "timestamp": "2024-09-12T14:30:00Z"
}

The most critical field for your pipeline is "summary"."pass". This boolean value provides a clear, automated signal: true if all metric thresholds were met, and false otherwise.

Step 4: Gate Your Deployment

The final step is to use the evaluation result as a quality gate. The shell script in Step 2 already does this: if the pass status is not true, it exits with an error code, failing the pipeline run.

Your deployment job would then be configured to only run if the evaluate-ai-agent job succeeds.

  deploy-to-production:
    runs-on: ubuntu-latest
    needs: evaluate-ai-agent
    steps:
      - name: Deploy to Production
        run: echo "Deploying AI agent to production..."
        # ... your deployment script here

With this setup, no AI update that causes a performance regression can be deployed automatically. You've successfully built a safety net for AI quality.

Ensure AI Quality, From Commit to Production

Integrating AI evaluations into your CI/CD pipeline transforms AI quality assurance from a manual, anxious process into an automated, confident one. By treating evaluations as code, you can catch regressions early, compare model performance objectively, and ensure that every AI feature you ship meets the highest standards of reliability and performance.

Gain confidence in your AI components. Stop guessing and start measuring.