The world of AI development is moving at a breakneck pace. Just a short while ago, a simple API call to an LLM was revolutionary. Today, developers are orchestrating complex, multi-step workflows using powerful frameworks like LangChain, LlamaIndex, and the OpenAI Assistants API. We're not just prompting models; we're building sophisticated AI agents that can reason, use tools, and interact with data.
This new "agentic stack" has unlocked incredible potential. But it has also introduced a critical new challenge: with so many moving parts, how do you know if your agent is actually working well? How do you measure improvements, prevent regressions, and prove its reliability?
This is the evaluation gap. And it's where Evals.do becomes the most critical new layer in your modern AI stack.
The modern AI stack is composed of several layers:
When you build an agent—say, for customer support—it might involve retrieving a customer's order history, analyzing their query, deciding which documentation to consult, and then drafting a helpful, empathetic response.
A small change to a single prompt template can have ripple effects across the entire workflow. How do you answer questions like:
Relying on a few manual spot-checks isn't scalable or reliable. You need a systematic way to measure performance.
Evals.do isn't another framework for building agents. It’s the platform you use to evaluate, score, and improve the agents you've already built. It provides the robust, quantitative feedback loop necessary for professional-grade AI development.
Think of it as the QA and testing layer purpose-built for AI. Instead of guessing, you get data. Here’s how Evals.do bridges the evaluation gap:
You can't improve what you can't measure. Evals.do allows you to move beyond "it feels better" by defining concrete, custom metrics that matter for your use case. You set the rules. For a customer support agent, you might define:
For each metric, you set a minimum passing threshold, creating a clear definition of "good enough" for production.
Manual testing is biased by the few examples you think to try. Evals.do systematizes testing by running your agent against a dataset—a consistent set of prompts and test cases. This ensures you're evaluating every version of your agent against the same benchmark, revealing true improvements or regressions over time.
Once an evaluation is complete, you don’t get a vague feeling. You get a clear, actionable report card. Evals.do provides an overall score and a breakdown for each metric you defined, instantly highlighting where your agent excels and where it falls short.
{
"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"
}
In this example, it's immediately obvious that while the agent is accurate and helpful, its tone needs work. This is the kind of insight that drives focused, effective iteration.
For professional development teams, the ultimate goal is to prevent regressions before they reach users. Evals.do integrates directly into your CI/CD pipeline via a simple API. You can automatically trigger an evaluation every time you push a change to your agent. If the evaluation score drops below your threshold, the build fails—stopping a low-quality change from ever being deployed. This is continuous integration for AI quality.
Evals.do is designed to work seamlessly with the tools you already use.
The era of treating LLM app development as a simple "prompt-and-pray" exercise is over. As AI agents become more autonomous and responsible for mission-critical tasks, a professional evaluation practice is no longer optional—it's essential.
Evals.do provides the dedicated, robust platform to implement that practice. It turns evaluation from a messy afterthought into a streamlined, integrated part of your development lifecycle.
Ready to stop guessing and start measuring? Sign up for free at Evals.do and run your first evaluation today.