A Practical Guide: Setting Up Your First AI Agent Evaluation with Evals.do

Building a powerful AI agent is an exciting achievement. But as you iterate, a critical question emerges: are your changes actually making it better? Gut feelings and one-off spot checks aren't enough to guarantee quality. A minor tweak to a prompt can cause unexpected regressions, and a new feature might degrade performance on core tasks. To build enterprise-grade AI, you need to move beyond hoping for the best and start quantifying performance with code.

This is where systematic evaluation comes in. Just as unit tests and integration tests provide a safety net for traditional software, AI evaluations ensure your agents, workflows, and functions are reliable, accurate, and safe.

Welcome to Evals.do, the platform designed to bring the rigor of software engineering to the world of AI development. This guide will walk you through setting up your very first AI agent evaluation, transforming quality from a subjective guess into an objective, measurable metric.

Why Systematic AI Evaluation is Non-Negotiable

In traditional software development, untested code is a liability. The same principle applies to AI, but the stakes can be even higher. An underperforming AI agent can erode user trust, provide dangerously incorrect information, or fail to complete critical business workflows.

Systematic evaluation helps you:

• Prevent Regressions: Confidently refactor prompts or update models, knowing your evaluation suite will catch any drop in performance.
• Build Trust: Provide stakeholders and users with concrete data that proves your AI meets the highest standards for quality and safety.
• Make Data-Driven Decisions: Objectively compare different models, prompts, or configurations to see which one truly performs best against your key business metrics.
• Accelerate Development: Automate the quality assurance process, allowing your team to innovate faster without sacrificing reliability.

Evals.do treats evaluation as a first-class citizen in the development lifecycle, enabling a practice we call Evaluation-Driven Development (EDD).

The Core Concepts: Evaluation as Code

Before we dive in, let's define a few core concepts in Evals.do. These are the building blocks of any evaluation.

• Target: This is the AI component you're testing. It could be a single LLM-powered function, a multi-step workflow, or a complex, autonomous agent. (e.g., customer-support-agent:v1.2).
• Dataset: This is your test suite. It's a collection of inputs (prompts, questions, scenarios) designed to challenge your Target across a range of expected interactions, including edge cases. (e.g., customer-support-queries-2024-q3).
• Metrics: These are the criteria for success, defined as code. They can be objective (e.g., factuality, contains_json) or subjective (e.g., helpfulness, tone). You set a pass/fail threshold for each metric.

Step-by-Step: Your First AI Agent Evaluation

Let's evaluate a hypothetical customer-support-agent. Our goal is to ensure it is accurate, helpful, and maintains a professional tone.

Step 1: Identify Your Target Agent

First, you need a stable, addressable version of your agent. Within your system, this might be a specific API endpoint, a Docker container tag, or a versioned agent name. For this example, our target is customer-support-agent:v1.2.

Step 2: Curate Your Evaluation Dataset

A good dataset is the heart of a great evaluation. It should represent the real-world challenges your agent will face. For our support agent, we'll create a dataset named customer-support-queries-2024-q3 containing entries like:

• "How do I reset my password?" (Standard query)
• "My order #ABC-123 hasn't arrived yet." (Requires data lookup)
• "I'm very frustrated, your last update broke my dashboard!" (Emotional user input)
• "Can you tell me about your refund policy and also compare your top three plans?" (Complex, multi-part query)

The more comprehensive your dataset, the more confidence you'll have in your evaluation results.

Step 3: Define Your Success Metrics

This is where you codify your quality standards. With Evals.do, you define metrics that will be used to grade the agent's response to each item in the dataset. You can use powerful LLM-based "model graders" or human reviewers to score performance.

For our agent, we'll define three key metrics:

1. Accuracy: Is the information provided factually correct? (Threshold: 4.0 out of 5)
2. Helpfulness: Does the response fully resolve the user's query? (Threshold: 4.2 out of 5)
3. Tone: Is the response professional, empathetic, and on-brand? (Threshold: 4.5 out of 5)

Step 4: Run the Evaluation

With the Target, Dataset, and Metrics defined, you trigger the evaluation via a simple API call. Evals.do orchestrates the entire process: it runs every query from your dataset against your agent, collects the responses, and grades each one against your defined metrics.

Step 5: Analyze the Results

Once complete, Evals.do provides a detailed report. This isn't just a simple pass/fail; it's a rich, quantitative summary of your agent's performance.

{
  "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"
}

From this output, we can see that our agent v1.2 passed the evaluation! The overall score is 4.35, and it met the individual thresholds for all three metrics. We can see its strongest point is tone (4.55), while accuracy (4.1) is just above the pass threshold, suggesting it's a potential area for improvement in the next development cycle.

From Manual to Automated: Integrating into Your CI/CD Pipeline

Running one evaluation is insightful. Automating it is transformative.

Because Evals.do is API-first, you can seamlessly integrate it into your existing CI/CD pipeline. This enables true Evaluation-Driven Development.

Imagine this workflow:

1. A developer pushes a change to the agent's prompt.
2. Your CI pipeline (e.g., GitHub Actions, Jenkins) automatically builds a new version, customer-support-agent:v1.3.
3. A new step in your pipeline makes an API call to Evals.do to run your evaluation suite against the new version.
4. The pipeline waits for the result.
5. If the evaluation passes, the agent is automatically deployed.
6. If the evaluation fails, the build is fails, preventing the regression from ever reaching production.

This closed-loop system ensures that no AI component that fails to meet your quality bar ever gets deployed.

Start Quantifying Your AI's Performance Today

Building reliable, high-quality AI is no longer an art; it's an engineering discipline. With a systematic approach to evaluation, you can gain deep confidence in your AI components and accelerate your development lifecycle.

Ready to stop guessing and start measuring? Visit Evals.do to gain confidence in your AI with rigorous, repeatable, and scalable evaluations.