---
title: "Continuous AI Evaluations in CI and Production"
description: "Run fast regression gates on every change and monitor sampled real-world outcomes without leaking user data."
language: en
canonical: https://navinresearch.com/blog/safety-evaluation/continuous-ai-evals.md
topics: ["continuous AI evaluation", "LLM evals CI", "production AI monitoring"]
---

# Continuous AI Evaluations in CI and Production

Run fast regression gates on every change and monitor sampled real-world outcomes without leaking user data.

**Short answer:** Use a layered eval program: small deterministic checks in each pull request, broader offline suites before release, canary comparisons during rollout, and privacy-reviewed production monitoring. Every failure should map to an owner and a release decision.

## Implementation steps

1. Version datasets, graders, prompts, tools, and model configuration.
2. Set blocking thresholds for critical task and safety slices.
3. Keep a private holdout set to detect overfitting.
4. Sample and redact production cases under a documented policy.

## Validation checklist

- Track confidence intervals and repeated-run variance.
- Alert on slice regressions even when the average improves.
- Verify rollback is fast and tested.

## Common mistakes

- One giant eval run nobody can interpret.
- Updating tests to make a failing release pass.
- Storing sensitive production traces indefinitely.

## Practical decision

Ship the smallest design that passes the checks above. Record the model, prompt, tool, dataset, and runtime versions used in testing so later changes can be compared rather than guessed. Recheck the linked documentation before relying on provider-specific limits, model names, prices, or preview features; those details change more quickly than the engineering principles in this note.

## Official sources

- [OpenAI, Evaluations guide](https://platform.openai.com/docs/guides/evals)
- [Anthropic, Demystifying evals for AI agents](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents)

---

Reviewed: 2026-08-24. This concise engineering note is educational, not a claim that Navin Research created or independently verified the cited provider technology.