AI is probabilistic. Your testing can't stay deterministic (docs.futureagi.com)

Future AGI’s evaluation SDK and a sample GitHub Actions workflow let teams run probabilistic model evaluations automatically on every pull request, turning model assessment into a CI/CD gate. The integration centers on four steps: Initialize an Evaluator with API keys, define eval_data (templates + inputs), submit the evaluation pipeline (params: project_name, version, eval_data), and retrieve results across versions (params: project_name, versions). The provided workflow (.github/workflows/evaluation.yml) plus evaluate_pipeline.py posts a formatted metrics comparison as a PR comment, enabling immediate visibility into regressions or improvements before merge. This matters because AI behavior is inherently probabilistic and deterministic unit tests can miss distributional shifts or performance regressions. Key implementation details: set GitHub Secrets (FI_API_KEY, FI_SECRET_KEY, PAT_GITHUB) and repo variables (PROJECT_NAME, VERSION, optional COMPARISON_VERSIONS), grant pull-requests: write permission so the action can comment, and tune max_wait_time and polling via get_pipeline_results for longer runs. Troubleshooting notes include verifying tokens, credentials, eval_data formats, and network access. Overall, embedding probabilistic evaluations into CI/CD gives ML teams consistent, versioned metrics, faster feedback loops, and stronger regression controls for model development and deployment.