Evaluating LLM Drift and Regression in Continuous Integration
How to set up automated model evaluation pipelines in GitHub Actions. We run synthetic gold-standard datasets against every PR to catch prompt degradation, tone shifts, and tool schema breaks before deployment.
- LLM provider model updates can silently alter reasoning behavior and output format
- CI test suites must evaluate both semantic accuracy and JSON schema conformity
- Threshold gating prevents broken agents from being merged into main branch
- Synthetic test datasets must mirror real production edge cases
When external AI providers quietly update model weights or temperature defaults, production software agents can silently change their reasoning behavior, output format, or tool invocation parameters.
To prevent prompt regression from reaching users, our CI/CD pipelines run automated evaluation suites on every pull request. Using frameworks like DeepEval, we test agent outputs against gold-standard evaluation datasets.
If semantic recall, schema conformity, or hallucination rates breach configured thresholds (e.g. >= 0.95), the build is failed and the pull request is blocked from merging.
steps:
- name: Run DeepEval Model Regression Suite
run: pytest tests/evals/test_agent_reasoning.py --threshold=0.95
env:
EVAL_API_KEY: ${{ secrets.EVAL_API_KEY }}OXELVO Engineering Group
The Systems Architecture & Applied AI team at OXELVO. We engineer resilient distributed backends, deterministic multi-agent state machines, and high-performance commercial software.
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