Catching the Drift: A Guide to Managing AI Model Degradation with Continuous Evaluation

Your new AI-powered customer support agent is a triumph. It’s helpful, accurate, and users love it. You push it to production and the team celebrates a successful launch. But weeks later, bug reports start trickling in. The agent is giving strange answers, misunderstanding simple queries, and its tone feels… off. What happened?

You've just encountered AI model degradation. It’s a silent but serious threat to any AI-powered application. The performance and quality of your once-perfect AI can decline over time, often without any direct changes to your code. This post explores why this happens and how you can implement a robust safety net using continuous, automated evaluation.

What Are Model Drift and Degradation?

While often used interchangeably, it's helpful to distinguish between two core concepts:

• Model Drift: This occurs when the real-world data your AI encounters changes over time. New slang, emerging customer issues, or shifts in user behavior can make the data your model was trained on obsolete. The model is the same, but the world has changed around it.
• Model Degradation: This is a drop in performance, even on a consistent dataset. The cause is often an external change to a component in your system. For complex agentic workflows, this is a major concern. A third-party API changes, a database schema is updated, or most commonly, the underlying LLM provider (like OpenAI or Anthropic) pushes an update to the model you're calling. That gpt-4-turbo you're using today isn't necessarily the exact same model it was last month.

The result is the same: your AI's quality erodes, leading to poor user experiences, factual inaccuracies, and potential damage to your brand.

Why Manual Spot-Checking Is a Failing Strategy

When problems arise, the first instinct is often to "spot-check"—to manually interact with the AI and see if you can replicate the issue. This is better than nothing, but as a long-term strategy, it’s destined to fail.

• It's Not Scalable: You can't manually test hundreds of edge cases after every single change.
• It's Biased: Testers are influenced by their own expectations and can't replicate the sheer variety of real-world user inputs.
• It's Not Repeatable: There's no consistent methodology, making it impossible to reliably compare performance over time.

Relying on spot-checking is like deploying critical backend code without a suite of unit and integration tests. It’s a gamble you can't afford to take.

The Solution: Continuous Evaluation in Your CI/CD Pipeline

To truly manage AI quality, you need to treat AI evaluations like a core part of your software development lifecycle. This is the principle behind Evaluation-Driven Development (EDD).

Instead of occasional manual checks, you define a comprehensive suite of tests that run automatically, flagging regressions before they ever reach production. This is where Evals.do comes in. By treating your evaluation criteria as code, you create a rigorous, repeatable, and scalable framework to quantify and enforce AI quality.

How to Catch Degradation with Evals.do

Integrating a platform like Evals.do turns quality from a hopeful outcome into a measurable requirement. Here’s the process:

1. Establish a Golden Dataset and Baseline

First, you capture a representative set of inputs and desired outcomes. This becomes your "golden dataset." You then run your current, high-performing AI agent against this dataset to establish a baseline score. Using Evals.do, you define the metrics that matter most to you—not just accuracy, but also helpfulness, tone, factuality, or any other custom dimension.

{
  "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 }
    }
  }
}

This report for v1.2 of our agent is our baseline. It passes all our quality thresholds.

2. Integrate Evaluations into CI/CD

This is the game-changer. With a simple API call, you can trigger a full evaluation run from your CI/CD pipeline (like GitHub Actions, Jenkins, or CircleCI). This happens automatically whenever a change is proposed that could impact your AI's behavior:

• Updating the agent's system prompt.
• Switching to a new model version (e.g., from gpt-4 to gpt-4o).
• Modifying a tool the agent uses.
• Even on a nightly schedule to detect upstream degradation from the model provider.

3. Automatically Block Regressions

The evaluation report provides a clear, quantitative pass/fail signal. In our example, we've set a threshold for each metric. If a new version of the agent, say v1.3, is pushed and the accuracy score drops to 3.9, the evaluation will fail.

The CI/CD pipeline can be configured to use this signal to block the merge or deployment. The developer is immediately notified that their change introduced a quality regression, complete with the specific examples that failed. They can debug and iterate until the agent once again meets the required quality bar.

A Practical Scenario

Imagine your team wants to update customer-support-agent to use a newer LLM that promises to be more conversational. They create a new version, v1.3, and open a pull request.

1. Trigger: The CI/CD pipeline automatically kicks off an evaluation run using Evals.do, testing v1.3 against the customer-support-queries-2024-q3 dataset.
2. Evaluate: The evaluation runs. The new model is indeed more conversational, so the tone score might even go up. However, it's also more "creative" and less grounded, causing it to hallucinate incorrect details about the company's return policy. The accuracy score drops below the threshold.
3. Block: Evals.do returns a pass: false result. The CI/CD pipeline fails the build and blocks the pull request from being merged.
4. Iterate: The developer gets an alert. They can now see exactly where the agent failed, adjust the system prompt to be more strict about factuality, and push a fix. The evaluation re-runs, and this time, all metrics pass. The code is now safe to merge.

Stop Guessing, Start Measuring

Model degradation isn't a possibility; it's an inevitability in the fast-moving world of AI. Relying on luck and manual checks is no longer a viable option.

By embracing a culture of continuous evaluation, you can build a safety net that protects your users and your business from the silent creep of quality decay. Platforms like Evals.do provide the tools to move from ambiguous, subjective assessments to a rigorous, code-based system that ensures your AI functions, workflows, and agents meet the highest standards with every single deployment.

Don't let your AI's performance degrade in silence. Gain confidence in your AI components by visiting Evals.do and start quantifying your AI performance with code.