You've built a groundbreaking AI agent. It’s smart, fast, and ready to change the world—or at least, your company's customer support. But a critical question lingers: how do you really know it's good? And more importantly, how do you ensure it stays good with every new update?
In the world of AI, "good" is a dangerously subjective term. An answer that one user finds helpful, another might find terse. A function that seems accurate in testing might fail spectacularly on unforeseen edge cases. To move from subjective feelings to objective facts, you need a robust framework for evaluation. The cornerstone of that framework is choosing the right metrics.
This guide will walk you through how to select meaningful AI metrics that accurately reflect the quality and performance of your AI agents, functions, and workflows.
For traditional machine learning models, success was often clear-cut. Metrics like accuracy, precision, and recall worked well for classification tasks. But for modern Large Language Models (LLMs) and agentic systems, the game has changed.
A generative AI's output is not just right or wrong; it exists on a spectrum of quality. Consider a customer support agent. An answer can be:
Relying on a single, simple metric like "accuracy" misses the entire picture. To truly understand performance, you need a multi-dimensional approach that captures the nuances of language, intent, and user experience.
Choosing the right metrics isn't about picking from a predefined list. It's about a systematic process of aligning your evaluation strategy with your product goals.
Start with the most fundamental question: What is this AI component supposed to accomplish? The answer should be specific and user-centric.
This single sentence already gives us several potential metric categories: accuracy, empathy (tone), and speed (efficiency).
Based on your AI's core job, brainstorm the different facets of what makes an output "high-quality." Group them into logical categories. Common dimensions include:
This is where theory meets practice. You need to turn abstract dimensions into quantifiable scores. For each dimension, define a specific metric with a clear scoring system (e.g., a scale of 1-5) and a passing threshold.
This is exactly what platforms like Evals.do are built for. You define the metrics that matter to you, and the platform handles the scoring against your test datasets using a combination of LLM-as-a-judge evaluators and human review.
An evaluation report for a customer support agent might look like this:
In this example, the agent is doing well on accuracy and helpfulness but failed the evaluation because its tone didn't meet the required standard. This is an actionable insight that a simple pass/fail accuracy test would have missed entirely.
Let's apply this framework to two different AI agents.
Choosing your metrics is the first step. The real power comes from applying them consistently. AI performance isn't static; a new model version or a change in your prompting strategy can cause unexpected regressions.
By integrating your evaluations into your CI/CD pipeline, you can automatically test every change against your curated datasets and quality metrics. Platforms like Evals.do provide simple APIs and SDKs to make this possible. This transforms evaluation from a one-off audit into a continuous safety net, allowing you to innovate quickly while ensuring quality and preventing regressions from ever reaching your users.
Ready to move beyond guesswork? Start by defining the metrics that truly matter for your AI. By quantifying quality, you gain the confidence to build, iterate, and deploy better, safer, and more effective AI agents.
Q: What can I evaluate with Evals.do?
A: 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.
Q: How are evaluations scored?
A: 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.
Q: Can I integrate Evals.do into my CI/CD pipeline?
A: 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.
{
"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"
}