In the world of traditional software, the CI/CD (Continuous Integration/Continuous Deployment) pipeline is our trusted gatekeeper. It runs tests, checks for bugs, and ensures that only high-quality, stable code makes it to production. But what happens when the "code" is a non-deterministic Large Language Model (LLM) or a complex AI agent?
A simple prompt tweak, a model update, or a change in a RAG system can have unforeseen consequences. The code still runs, but the AI's tone might become unprofessional, its answers less accurate, or its behavior completely unexpected. This is the new frontier of software quality, and it requires a new kind of gatekeeper.
Enter continuous AI evaluation. By integrating a robust evaluation platform like Evals.do directly into your CI/CD pipeline, you can automate quality assurance for your AI, catch regressions before they impact users, and ship better AI, faster.
Your unit tests are great at confirming your application's logic. They can verify that an API call is made or that a function returns a string. What they can't do is tell you if that string is helpful, accurate, or safe.
This creates a critical gap where "silent failures" can occur:
Relying on manual spot-checking is slow, inconsistent, and doesn't scale. To build enterprise-grade AI, you need an automated, objective, and repeatable way to measure quality.
Evals.do is a platform built to quantify the performance of AI agents, functions, and workflows. It provides the critical tooling to run evaluations automatically, making it a perfect fit for any modern development pipeline.
Integrating AI evaluation into CI/CD involves three core steps, all streamlined by Evals.do.
Before you can test your AI, you must define what "good" looks like.
This is where the magic happens. Evals.do is designed to be automated. With a simple API and SDKs, you can add an "AI Quality" stage to your CI/CD workflow (e.g., in GitHub Actions, Jenkins, or CircleCI).
The process looks like this:
Evals.do runs your agent against the test cases and scores its performance against your metrics using a combination of LLM-as-a-judge evaluators and human feedback loops. It then returns a structured result.
Consider this example evaluation report from the Evals.do API:
{
"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 is a powerful tool for your pipeline. The passed: false field is a clear signal. In this case, while the agent's accuracy and helpfulness were acceptable, its tone score of 3.55 fell below the required threshold of 4.5.
Your CI/CD job can parse this response and automatically fail the build. The bad AI is stopped in its tracks, the developer is notified immediately with concrete feedback, and your users are protected from a subpar experience.
Integrating Evals.do into your development lifecycle isn't just a defensive measure; it's a catalyst for innovation.
In the age of AI, "it works on my machine" is no longer enough. We need to be able to prove that our AI is effective, safe, and high-quality. By embedding evaluation directly into the development process, you can build a culture of quality and ensure you never ship a bad AI again.
Ready to bring robust AI evaluation to your CI/CD pipeline? Visit Evals.do to learn more and simplify your AI testing.
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.