We're in the golden age of AI agents. From sophisticated customer support bots that resolve issues in real-time to complex workflows that analyze data and generate reports, the potential is immense. But as these systems grow in complexity, so does the risk of failure. A helpful agent might adopt the wrong tone, or an accurate one might miss a subtle but critical safety guardrail. How do you build robust, reliable agents that you can trust in production?
The answer doesn't lie in just testing the final output. The secret to building great macro-level agents is the rigorous, systematic evaluation of their micro-level components: the individual AI functions that form their foundation.
Before we dive into evaluation, it's crucial to understand that a modern AI agent is rarely a single, monolithic model call. It's a system—a workflow or a chain of smaller, specialized, LLM-powered functions working in concert.
Consider a customer support agent. Its workflow might look like this:
Each step is a distinct function. And the performance of the entire agent depends entirely on the quality and reliability of each of these building blocks.
In traditional software, we have unit tests for a reason. A small bug in a single function can bring down an entire application. The same principle applies to AI, but the consequences can be more insidious. This is the danger of "error propagation."
Imagine our support agent's Intent Classification Function is 95% accurate. That sounds pretty good. But for 5% of users, it makes a mistake. For example, it might misclassify a "cancellation request" as a "billing inquiry."
The user is frustrated, and the business may have lost a customer. Even though the other functions performed their tasks "correctly" based on the input they received, the entire interaction was a failure because of one weak link at the very beginning. Focusing only on the final output makes it incredibly difficult to pinpoint why the failure occurred.
By evaluating each AI function individually, you gain the clarity and control needed to build truly robust systems. This approach, akin to unit testing in software development, offers several key advantages:
This is precisely the philosophy behind Evals.do. We believe that robust agent evaluation starts with powerful AI function evaluation.
Evals.do is designed to facilitate this micro-to-macro evaluation strategy. Our platform allows you to move beyond vague assessments and quantify the performance of every part of your AI system.
You define what "good" means with custom metrics and passing thresholds. Then, you can run evaluations against consistent datasets to reliably measure model performance.
Let's look at an evaluation report from Evals.do for our customer support agent. Here, we're seeing the results for the overall agent, but the insights are granular.
{
"evaluationId": "eval_8a7d6e8f4c",
"agentId": "customer-support-agent-v2",
"status": "completed",
"overallScore": 4.15,
"passed": false,
"metrics": [
{
"name": "accuracy",
"score": 4.3,
"threshold": 4.0,
"passed": true
},
{
"name": "helpfulness",
"score": 4.6,
"threshold": 4.2,
"passed": true
},
{
"name": "tone",
"score": 3.55,
"threshold": 4.5,
"passed": false
}
],
"evaluatedAt": "2024-10-27T10:30:00Z"
}
This JSON output tells a clear story. The agent is accurate and helpful, easily passing those checks. However, the overall evaluation failed. Why? Because the tone score of 3.55 fell below the required threshold of 4.5. This immediately tells the developer where to focus their efforts—not on the logic or knowledge base, but on the prompts and models governing the agent's conversational style.
This level of detailed AI testing is impossible when you only look at the final conversation.
Once you've validated each individual function, you can scale up your evaluations to test how they work together in a full workflow. This combination of "unit" (function) and "integration" (agent) testing gives you a comprehensive view of your system's quality.
Better yet, by integrating Evals.do into your CI/CD pipeline via our API, you can automate this entire process. Every code change can trigger a new evaluation, ensuring that you're not just preventing bugs, but continuously improving the quality and safety of your AI.
Don't let the complexity of modern agents lead to unpredictable and unreliable behavior. Embrace a disciplined, bottom-up approach. Start by evaluating your individual functions, and you'll build macro-level agents that are not only powerful but also predictable, safe, and trustworthy.
What can I evaluate with Evals.do?
You can evaluate any AI component, from individual LLM-powered functions and complex agentic workflows to full conversational agents. The platform is designed to be flexible and adaptable to your specific needs.
How are evaluations scored?
You define custom metrics (like accuracy, tone, or relevance) with specific scales and passing thresholds. Evals.do can use a combination of automated LLM-as-a-judge evaluators and human review to score performance against your defined criteria.
Can I integrate Evals.do into my CI/CD pipeline?
Yes, Evals.do provides a simple API and SDKs, making it easy to trigger evaluations automatically as part of your CI/CD pipeline. This allows you to continuously monitor and prevent AI performance regressions before they reach production.