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The framework emitted raw signals but had no operational layer turning them into an SLO. Add evaluate_slo (SLI + error budget), burn_rate, and multi-window multi-burn-rate alerts (Google SRE workbook tiers) over outcome records. Records are plain data and the clock is injectable, so it is fully deterministic. Wired through the facade, AC_evaluate_slo/ AC_burn_alerts executor commands, MCP tools and the Script Builder.
Up to standards ✅🟢 Issues
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| Metric | Results |
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| Complexity | 42 |
| Duplication | 0 |
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Summary
The framework emits raw signals (
observabilitymetrics,run_historydurations) but had no operational layer turning them into an SLO, an error budget, or burn-rate alerts. This adds that.evaluate_slo(records, target, *, window_s, now)→{sli, good, total, budget_total, budget_remaining, budget_remaining_fraction, burn_rate}.burn_rate(...)— the bare burn rate over a window.burn_alerts(records, target, *, rules, now)— multi-window multi-burn-rate alerts;default_burn_rules()are the Google SRE tiers (page 14.4×@1h/5m, page 6×@6h/30m, ticket 1×@3d/6h), firing only when both windows exceed the threshold.BurnRuleto customise.Records are plain data (
[{"timestamp", "ok"}]), clock injectable → fully deterministic. Pure stdlib.Five-layer wiring
je_auto_control/utils/slo/__init__.py+__all__AC_evaluate_slo,AC_burn_alertsac_evaluate_slo,ac_burn_alertsTests & docs
test/unit_test/headless/test_slo_batch.py(11 tests: budget math, burn rate, windowing, multi-window alerts fire/quiet, bad target)Lint clean: ruff / pylint / bandit / radon.